Show of Hands:Votes: Party classification:: Q109244Yes_AllX_cnk01_rest
bdanalytics
Date: (Wed) Jun 15, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
# '(glbObsAll[, "Q109244"] != "")' # NA
'(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "Yes"))' # Yes
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(4, 6, 7, 8, 9, 10, 16, 32, 64, 128, 253) # accuracy(8) = 0.5648
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
# glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["All.X"]] <- c("glmnet")
# glbMdlFamilies[["All.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# , "lda" # error: model fit failed for Fold1.Rep1: parameter=none Error in lda.default(x, grouping, ...)
# ,"lda2" # error: There were missing values in resampled performance measures.
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
# # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlSequential <- c(NULL
, "All.X#zv.pca#rcv#glmnet"
)
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
AllX__rcv_glmnetTuneParams <- rbind(data.frame() # alpha shd be <= 1.0 ALWAYS
,data.frame(parameter = "alpha", vals = "0.325 0.550 0.775 0.9 1.000")
,data.frame(parameter = "lambda", vals = "1.034113e-03 4.799925e-03 2.227928e-02 0.04 0.06")
) # max.Accuracy.OOB = 0.7875648 @ 0.55 0.04
# AllX_nzv_rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "1.842462e-02 0.03287977 0.04733492 0.06179007 0.07624522")) # max.Accuracy.OOB = 0.7875648 @ 0.55 0.06179007 @ 0.55 0.04733492 @ 0.775 0.03287977 @ 1 0.01842462
# AllX_zvpca_rcv_glmnetTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(parameter = "lambda", vals = "1.847495e-02 0.02 0.03296959 0.04 0.05")) # max.Accuracy.OOB = 0.7927461 @ 1 0.01847495
# # 0.7875648 @ 0.775 0.03296959
#
glbMdlTuneParams <- rbind(glbMdlTuneParams
,cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams)
# ,cbind(data.frame(mdlId = "All.X#nzv#rcv#glmnet"), AllX_nzv_rcv_glmnetTuneParams)
# ,cbind(data.frame(mdlId = "All.X#zv.pca#rcv#glmnet"),
# AllX_zvpca_rcv_glmnetTuneParams)
)
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
# ,data.frame(parameter = "degree", vals = "1")
# ,data.frame(parameter = "nprune", vals = "256")
# )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
# bagEarthTuneParams))
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(NULL # NULL # : default
# ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.YeoJohnson.pca")))
# ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
# c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
# # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
# , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
, "LGOCV"
, "adaptive_cv" # crashed for Q109244No
# , "adaptive_boot" #error: adaptive$min should be less than 3
# , "adaptive_LGOCV" #error: adaptive$min should be less than 3
)
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- # NULL #: default
c("Q109244NA_Ensemble_cnk03_rest_out_fin.csv")
# c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv")
# c("Votes_Ensemble_cnk06_out_fin.csv")
glbOut <- list(pfx = "Q109244Yes_AllX_cnk01_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "cluster.data" # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/Q109244Yes_AllX_cnk01_manage.missing.data_manage.missing.data.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 cluster.data 1 0 0 9.273 NA NAStep 1.0: cluster data
chunk option: eval=
Step 1.0: cluster data
Step 1.0: cluster data
```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
Step 1.0: cluster data
## Loading required package: proxy##
## Attaching package: 'proxy'## The following objects are masked from 'package:stats':
##
## as.dist, dist## The following object is masked from 'package:base':
##
## as.matrix## Loading required package: dynamicTreeCut## Loading required package: entropy## Loading required package: tidyr## Loading required package: ggdendro## [1] "Clustering features: "## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero## abs.cor.y
## Q121699.fctr 0.06186040
## Q114517.fctr 0.06233932
## Q124122.fctr 0.06976947
## Q114386.fctr 0.07613008
## Q114152.fctr 0.07783674
## [1] " .rnorm abs(cor): 0.0102"
## [1] " Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.4974"## Loading required package: lazyeval## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 40 9 0.4769183 49
## 2 MKn 1 MKn_1 97 26 0.5157821 123
## 3 MKy 1 MKy_1 186 43 0.4829775 229
## 4 PKn 1 PKn_1 45 5 0.3250830 50
## 5 PKy 1 PKy_1 10 1 0.3046361 11
## 6 SKn 1 SKn_1 325 92 0.5276960 417
## 7 SKy 1 SKy_1 39 7 0.4264615 46
## [1] "glbObsAll$Hhold.fctr Entropy: 0.4937 (99.2605 pct)"## [1] "Category: N"
## [1] "max distance(0.9741) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4156 5186 D N NA NA NA
## 4978 6219 D N NA NA No
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4156 NA NA NA NA NA
## 4978 NA Pt Yes No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4156 NA NA NA NA Yes
## 4978 No Yes NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4156 Art Yes NA NA No
## 4978 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4156 NA Giving NA No Yes
## 4978 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4156 Yes Id No Standard hours Cool headed
## 4978 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4156 Yes NA No NA NA
## 4978 NA NA Yes NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4156 NA NA NA NA NA
## 4978 NA NA Yes Start No
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4156 NA NA NA NA NA
## 4978 No Cs Yes No Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4156 NA NA NA NA NA
## 4978 Yes No TMI NA Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4156 NA NA NA NA NA
## 4978 Tunes Technology No Yes No
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4156 NA NA NA NA NA
## 4978 NA Yes NA Yes Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4156 No Yes Risk-friendly Yes! NA
## 4978 NA Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4156 NA Yes In-person NA No
## 4978 NA NA NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4156 Yes NA Yy NA NA
## 4978 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4156 NA NA Yes Yes Yes
## 4978 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4156 No No NA Yes Yes
## 4978 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4156 NA NA NA NA NA
## 4978 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4156 NA NA Yes NA NA
## 4978 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4156 NA NA Yes Yes Yes
## 4978 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4156 Yes NA Yes
## 4978 NA NA NA
## [1] "min distance(0.9626) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1348 1671 D N NA No No
## 4406 5498 D N NA No No
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1348 Yes Pc No No No
## 4406 No Pc Yes Yes No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1348 No Yes Yes Yes No
## 4406 No No Yes No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1348 Art Yes Try first No Yes
## 4406 Science No Study first Yes Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1348 No Giving No No Yes
## 4406 No Giving No NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1348 No Id Yes Standard hours Cool headed
## 4406 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1348 No Happy No Yes No
## 4406 No Happy Yes Yes No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1348 Yes P.M. Yes End Yes
## 4406 No P.M. Yes NA Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1348 No Cs Yes No No
## 4406 Yes NA Yes Yes No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1348 Yes Yes TMI Yes No
## 4406 No Yes TMI Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1348 Tunes People Yes Yes Yes
## 4406 Tunes Technology Yes Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1348 No Yes Supportive Yes Mac
## 4406 No No Supportive No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1348 No Yes Risk-friendly Yes! Yes
## 4406 NA Yes Cautious Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1348 Socialize Yes In-person No No
## 4406 Space No NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1348 Yes Yes Gr Yes No
## 4406 NA Yes Yy Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1348 Yes Yes Yes Yes Yes
## 4406 Yes Yes Yes Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1348 Yes No No Yes No
## 4406 Yes No Yes Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1348 Own Optimist Mom No No
## 4406 Own Optimist Mom Yes Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1348 Yes Yes No Nope No
## 4406 Yes Yes Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1348 Yes No Yes Yes Yes
## 4406 No Yes Yes No Yes
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1348 Yes Yes No
## 4406 Only-child Yes No
## [1] "Category: MKn"
## [1] "max distance(0.9742) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3967 4944 D MKn Yes Yes Yes
## 5755 969 <NA> MKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3967 No NA NA NA NA
## 5755 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3967 NA NA No NA NA
## 5755 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3967 NA No Study first No No
## 5755 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3967 No Receiving No Yes No
## 5755 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3967 No Pr Yes Odd hours Hot headed
## 5755 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3967 No NA Yes Yes Yes
## 5755 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3967 Yes P.M. NA NA Yes
## 5755 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3967 No Me NA NA NA
## 5755 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3967 NA NA Mysterious NA Yes
## 5755 NA NA NA No No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3967 Tunes People No NA Yes
## 5755 Talk People No Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3967 NA NA NA NA NA
## 5755 Yes Yes Supportive NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3967 No Yes Risk-friendly Yes! NA
## 5755 Yes Yes Cautious Umm... Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3967 Space No In-person Yes No
## 5755 Space Yes In-person Yes No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3967 Yes NA NA NA NA
## 5755 Yes Yes Gr Yes Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3967 NA NA NA NA NA
## 5755 No Yes Yes Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3967 NA NA NA NA NA
## 5755 Yes No No Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3967 NA NA NA NA NA
## 5755 Rent Pessimist Dad Yes No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3967 NA NA NA NA NA
## 5755 Yes Yes Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3967 NA NA NA NA NA
## 5755 Yes Yes No Yes No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3967 NA NA NA
## 5755 Yes Yes Yes
## [1] "min distance(0.9614) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1370 1704 D MKn No No No
## 3679 4588 D MKn Yes No Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1370 No Pt Yes No No
## 3679 No Pt No Yes No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1370 No Yes No No Yes
## 3679 No Yes No No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1370 Science No Study first No Yes
## 3679 Art NA Study first No Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1370 No Giving Yes Yes No
## 3679 No Giving No Yes Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1370 No Id No Odd hours Cool headed
## 3679 No Id No Standard hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1370 No Happy Yes Yes No
## 3679 Yes Happy Yes Yes No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1370 Yes A.M. No Start Yes
## 3679 Yes P.M. Yes Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1370 Yes Cs Yes Yes No
## 3679 Yes Me Yes Yes Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1370 Yes Yes TMI Yes Yes
## 3679 Yes Yes TMI Yes Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1370 Tunes People No Yes Yes
## 3679 Tunes Technology Yes Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1370 Yes No Supportive No Mac
## 3679 No No Demanding No PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1370 No Yes Risk-friendly Umm... No
## 3679 Yes Yes Cautious Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1370 Socialize No In-person No No
## 3679 Space No In-person No Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1370 Yes Yes Yy Yes No
## 3679 Yes Yes Gr Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1370 Yes Yes No No Yes
## 3679 No No Yes Yes No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1370 No No No Yes No
## 3679 No No No Yes Yes
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1370 Own Optimist Dad Yes Yes
## 3679 Own Optimist Dad No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1370 No Yes Yes Check! No
## 3679 Yes Yes Yes Check! No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1370 No Yes No Yes No
## 3679 Yes Yes No Yes Yes
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1370 Yes Yes No
## 3679 Yes Yes No
## [1] "Category: MKy"
## [1] "max distance(0.9761) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 132 167 D MKy NA NA NA
## 4451 5552 D MKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 132 NA NA NA NA NA
## 4451 No Pc No No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 132 NA NA NA NA NA
## 4451 No Yes Yes No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 132 NA NA NA NA NA
## 4451 Science Yes NA Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 132 NA NA NA NA NA
## 4451 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 132 NA NA NA NA NA
## 4451 NA NA NA Standard hours Hot headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 132 NA NA NA NA NA
## 4451 No Happy Yes Yes No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 132 NA NA NA NA NA
## 4451 No A.M. Yes Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 132 NA NA NA NA NA
## 4451 No Me No Yes No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 132 NA NA NA NA NA
## 4451 Yes NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 132 NA NA NA No Yes
## 4451 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 132 No Yes Demanding No Mac
## 4451 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 132 Yes Yes Cautious Yes! No
## 4451 Yes Yes Risk-friendly Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 132 Space No Online Yes Yes
## 4451 Space No In-person No Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 132 No Yes Gr Yes No
## 4451 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 132 Yes No Yes Yes Yes
## 4451 NA Yes No Yes NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 132 Yes Yes No Yes Yes
## 4451 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 132 Own Pessimist Mom Yes Yes
## 4451 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 132 Yes No Yes Check! No
## 4451 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 132 No Yes No No No
## 4451 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 132 Yes Yes Yes
## 4451 NA NA NA
## [1] "min distance(0.9604) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4900 6123 D MKy NA Yes No
## 6791 6120 <NA> MKy NA Yes NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4900 No Pc Yes Yes No
## 6791 NA Pc No No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4900 No Yes No No Yes
## 6791 No Yes No Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4900 Art No Try first Yes Yes
## 6791 Science No Try first Yes Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4900 Yes Giving No Yes No
## 6791 No Giving Yes Yes Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4900 No Id Yes Odd hours Cool headed
## 6791 Yes Id No Standard hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4900 Yes Happy Yes Yes No
## 6791 No NA NA Yes Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4900 No P.M. Yes Start Yes
## 6791 No P.M. Yes Start No
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4900 Yes Me Yes No No
## 6791 No Cs Yes Yes No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4900 Yes Yes TMI No Yes
## 6791 No Yes TMI Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4900 Tunes Technology No Yes No
## 6791 Tunes Technology NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4900 Yes Yes Demanding No PC
## 6791 NA NA NA No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4900 Yes Yes Cautious Umm... No
## 6791 Yes Yes Cautious Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4900 Space No Online No Yes
## 6791 Space No In-person Yes Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4900 Yes Yes Yy Yes No
## 6791 Yes No Yy Yes Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4900 Yes No Yes No No
## 6791 Yes No No Yes No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4900 NA NA NA NA NA
## 6791 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4900 NA NA NA NA NA
## 6791 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4900 NA NA NA NA NA
## 6791 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4900 NA NA NA NA NA
## 6791 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4900 NA NA NA
## 6791 NA NA NA
## [1] "Category: PKn"
## [1] "max distance(0.9743) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 884 1099 D PKn NA NA NA
## 1645 2038 D PKn NA NA Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 884 NA NA NA NA NA
## 1645 NA Pc Yes NA No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 884 NA NA NA NA NA
## 1645 No Yes Yes NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 884 NA NA NA NA NA
## 1645 NA NA Study first NA Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 884 NA NA NA NA NA
## 1645 No Giving Yes NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 884 NA NA NA NA NA
## 1645 NA NA NA Standard hours NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 884 NA NA NA NA NA
## 1645 NA NA Yes NA No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 884 NA NA NA NA NA
## 1645 No NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 884 NA NA NA NA NA
## 1645 No NA NA NA Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 884 NA NA NA NA NA
## 1645 No NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 884 NA NA NA NA NA
## 1645 NA NA No NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 884 NA NA NA No Mac
## 1645 NA Yes NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 884 No Yes Risk-friendly Yes! No
## 1645 No Yes NA NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 884 Socialize Yes In-person No Yes
## 1645 NA NA NA No NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 884 Yes Yes Yy Yes No
## 1645 Yes NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 884 Yes Yes Yes Yes No
## 1645 NA No NA Yes NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 884 Yes Yes No No No
## 1645 Yes NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 884 Own NA NA Yes NA
## 1645 Own Pessimist NA Yes Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 884 NA NA NA NA NA
## 1645 NA Yes NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 884 NA NA Yes No Yes
## 1645 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 884 Yes No No
## 1645 NA NA Yes
## [1] "min distance(0.9617) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 993 1236 R PKn No Yes No
## 4354 5436 D PKn Yes Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 993 No Pc Yes No No
## 4354 No Pc No No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 993 No Yes Yes Yes NA
## 4354 No Yes Yes Yes No
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 993 NA NA NA NA NA
## 4354 Science No Study first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 993 NA Giving No No NA
## 4354 No Giving Yes No NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 993 NA NA NA NA NA
## 4354 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 993 NA NA NA NA NA
## 4354 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 993 NA P.M. Yes Start Yes
## 4354 NA P.M. No Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 993 No Cs Yes Yes Yes
## 4354 Yes Me Yes NA Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 993 Yes Yes TMI No No
## 4354 No Yes TMI No Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 993 Tunes People No Yes Yes
## 4354 Tunes People No Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 993 No NA NA NA Mac
## 4354 No No Supportive No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 993 Yes Yes Cautious Umm... NA
## 4354 Yes Yes Risk-friendly Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 993 Space No In-person Yes No
## 4354 Socialize No NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 993 Yes Yes Gr Yes Yes
## 4354 NA No Yy Yes Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 993 Yes No Yes No No
## 4354 Yes No Yes No Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 993 No No Yes No Yes
## 4354 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 993 Rent Pessimist Dad NA Yes
## 4354 NA Optimist Mom NA Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 993 Yes Yes Yes Check! No
## 4354 Yes Yes Yes Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 993 No Yes Yes No No
## 4354 No Yes Yes No NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 993 Yes Yes Yes
## 4354 NA NA NA
## [1] "Category: PKy"
## [1] "max distance(0.9685) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3626 4516 D PKy NA NA NA
## 6815 6244 <NA> PKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3626 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3626 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3626 NA NA NA Yes No
## 6815 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3626 No NA NA NA Yes
## 6815 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3626 Yes Pr Yes Standard hours Cool headed
## 6815 NA NA NA NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3626 No Happy Yes No No
## 6815 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3626 Yes A.M. No End Yes
## 6815 NA NA NA NA NA
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3626 No Cs Yes Yes Yes
## 6815 NA NA NA NA NA
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3626 Yes No TMI No No
## 6815 NA NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3626 Talk People No Yes Yes
## 6815 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3626 Yes Yes Supportive No Mac
## 6815 NA NA NA NA PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3626 Yes Yes Cautious Umm... No
## 6815 Yes Yes Risk-friendly Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3626 Space No In-person Yes Yes
## 6815 Space No NA NA NA
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3626 Yes Yes Yy Yes No
## 6815 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3626 Yes Yes No No No
## 6815 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3626 No No Yes Yes No
## 6815 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3626 Rent Optimist Dad No Yes
## 6815 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3626 Yes Yes Yes Check! No
## 6815 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3626 No No Yes No No
## 6815 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 3626 Yes No NA
## 6815 NA NA NA
## [1] "min distance(0.9623) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 501 626 R PKy NA No Yes
## 950 1181 D PKy NA Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 501 No Pc Yes No No
## 950 No Pc No No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 501 No Yes Yes Yes No
## 950 No Yes No Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 501 Art Yes Study first Yes Yes
## 950 Science No Study first Yes Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 501 No Giving No Yes No
## 950 Yes Giving No Yes Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 501 No Id No Odd hours Cool headed
## 950 No Id Yes Standard hours Hot headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 501 Yes Happy Yes Yes No
## 950 Yes Happy No No Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 501 No P.M. No Start Yes
## 950 Yes P.M. No Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 501 No Cs Yes No Yes
## 950 No Me Yes No Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 501 Yes No TMI No Yes
## 950 No Yes TMI Yes Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 501 Tunes People No No Yes
## 950 Talk Technology No No No
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 501 Yes No Supportive No Mac
## 950 Yes Yes Supportive No Mac
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 501 Yes Yes Risk-friendly Umm... No
## 950 No Yes Cautious Umm... No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 501 Space No In-person No Yes
## 950 Space No Online Yes No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 501 Yes No Yy Yes Yes
## 950 Yes Yes Gr Yes Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 501 No Yes Yes No No
## 950 No Yes Yes Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 501 No No Yes Yes No
## 950 No Yes Yes Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 501 Own Optimist Mom Yes Yes
## 950 Rent Optimist Mom No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 501 No Yes Yes Nope No
## 950 No Yes Yes Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 501 No No No No No
## 950 No Yes Yes Yes No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 501 Yes Yes Yes
## 950 Yes No Yes
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: SKn"
## [1] "max distance(0.9759) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2209 2754 D SKn NA NA NA
## 5925 1841 <NA> SKn NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2209 No Pc Yes NA NA
## 5925 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2209 No Yes Yes No Yes
## 5925 NA NA NA NA NA
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2209 Science Yes Study first NA NA
## 5925 NA NA NA NA NA
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2209 No NA NA Yes NA
## 5925 NA NA NA NA NA
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 2209 No Id No Standard hours NA
## 5925 NA NA NA Odd hours Hot headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2209 NA NA NA NA NA
## 5925 Yes NA No Yes Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2209 NA NA NA NA NA
## 5925 NA P.M. No Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2209 NA NA NA NA NA
## 5925 No Cs Yes Yes No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2209 NA NA NA NA NA
## 5925 No NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2209 NA People Yes NA NA
## 5925 NA NA NA NA NA
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2209 NA Yes NA Yes Mac
## 5925 NA NA NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2209 NA Yes NA Yes! NA
## 5925 Yes Yes Risk-friendly Umm... NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2209 Socialize No NA NA NA
## 5925 Space No In-person No No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2209 Yes Yes NA NA NA
## 5925 NA NA Yy Yes No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2209 No Yes NA Yes NA
## 5925 NA No No No Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2209 Yes NA No Yes NA
## 5925 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2209 Rent Optimist Dad Yes Yes
## 5925 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2209 Yes Yes Yes Check! NA
## 5925 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2209 No Yes Yes No NA
## 5925 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 2209 Yes NA Yes
## 5925 NA NA NA
## [1] "min distance(0.9611) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4937 6165 D SKn Yes Yes Yes
## 5572 7 <NA> SKn Yes Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4937 NA Pc Yes Yes Yes
## 5572 No Pc Yes Yes No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4937 No No No Yes Yes
## 5572 No Yes No Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4937 NA Yes Study first NA Yes
## 5572 Science Yes Try first No Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4937 Yes Receiving NA Yes Yes
## 5572 Yes Giving No Yes No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4937 No Pr No Standard hours Cool headed
## 5572 No Id Yes Standard hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4937 NA NA Yes Yes Yes
## 5572 No Happy Yes No No
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4937 Yes NA Yes NA No
## 5572 Yes A.M. Yes Start Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4937 NA Cs No NA No
## 5572 No Me Yes Yes No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4937 No Yes TMI Yes No
## 5572 Yes Yes TMI Yes No
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4937 Tunes People No Yes Yes
## 5572 Talk People No No Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4937 Yes Yes Supportive No PC
## 5572 No Yes Supportive No PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4937 No Yes Cautious Umm... No
## 5572 No Yes Cautious Yes! No
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4937 Space No NA No No
## 5572 Space No Online No No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4937 Yes Yes Gr No No
## 5572 Yes Yes Yy No No
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4937 NA NA NA NA NA
## 5572 No No No Yes Yes
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4937 NA NA NA NA NA
## 5572 No No No No No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4937 NA NA NA NA NA
## 5572 Own Optimist Dad No No
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4937 NA NA NA NA NA
## 5572 Yes Yes Yes Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4937 NA NA NA NA NA
## 5572 No No Yes No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 4937 NA NA NA
## 5572 Yes No Yes
## [1] "Category: SKy"
## [1] "max distance(0.9757) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1392 1730 D SKy NA NA NA
## 1512 1872 D SKy NA NA NA
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1392 NA NA NA NA NA
## 1512 NA NA NA NA NA
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1392 NA NA NA NA NA
## 1512 NA NA Yes Yes Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1392 NA NA NA NA NA
## 1512 NA No Study first No Yes
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1392 NA NA NA NA NA
## 1512 Yes Giving No Yes Yes
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1392 NA NA NA NA NA
## 1512 No Id Yes NA NA
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1392 NA NA NA NA NA
## 1512 NA NA NA NA NA
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1392 NA NA NA NA NA
## 1512 NA NA Yes End Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1392 NA NA NA NA NA
## 1512 No NA Yes Yes No
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1392 NA NA NA NA NA
## 1512 No NA NA NA NA
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1392 NA NA NA NA NA
## 1512 NA NA NA No No
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1392 NA NA NA Yes PC
## 1512 NA Yes NA NA NA
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1392 Yes Yes Cautious Yes! No
## 1512 Yes Yes Risk-friendly NA NA
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1392 Space No Online Yes Yes
## 1512 Space No Online Yes Yes
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1392 Yes Yes Yy No Yes
## 1512 NA NA NA NA NA
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1392 Yes No Yes Yes Yes
## 1512 NA NA NA NA NA
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1392 Yes Yes No No No
## 1512 NA NA NA NA NA
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1392 Rent Optimist Dad No Yes
## 1512 NA NA NA NA NA
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1392 Yes Yes Yes Check! No
## 1512 NA NA NA NA NA
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1392 Yes No Yes Yes No
## 1512 NA NA NA NA NA
## Q98059.fctr Q98078.fctr Q96024.fctr
## 1392 Yes Yes No
## 1512 NA NA NA
## [1] "min distance(0.9613) pair:"
## USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 99 122 D SKy No Yes Yes
## 769 952 D SKy No Yes Yes
## Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 99 No Pc No Yes Yes
## 769 No Pc Yes No No
## Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 99 Yes Yes Yes Yes Yes
## 769 No Yes Yes No Yes
## Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 99 Science No Study first No Yes
## 769 Art No Study first Yes No
## Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 99 Yes Giving Yes No Yes
## 769 Yes Giving No Yes No
## Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 99 Yes Pr No Standard hours Hot headed
## 769 Yes Pr No Odd hours Cool headed
## Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 99 No Happy Yes Yes No
## 769 Yes Happy No No Yes
## Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 99 Yes P.M. Yes End Yes
## 769 No P.M. Yes End Yes
## Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 99 No Me Yes Yes Yes
## 769 No Cs Yes No Yes
## Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 99 No No TMI Yes Yes
## 769 No No TMI Yes Yes
## Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 99 Tunes People Yes Yes Yes
## 769 Tunes Technology No Yes Yes
## Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 99 No Yes Demanding Yes PC
## 769 No Yes Demanding Yes PC
## Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 99 Yes Yes Cautious Umm... Yes
## 769 Yes Yes Cautious Umm... Yes
## Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 99 Space No Online Yes No
## 769 Space No Online Yes No
## Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 99 Yes No Yy No Yes
## 769 Yes Yes Gr No Yes
## Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 99 No Yes Yes Yes No
## 769 No Yes Yes Yes No
## Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 99 Yes Yes Yes No NA
## 769 Yes Yes Yes Yes No
## Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 99 Rent NA NA No Yes
## 769 Rent Pessimist Dad No Yes
## Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 99 Yes NA Yes Check! NA
## 769 No Yes No Nope No
## Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 99 NA NA No No NA
## 769 No Yes No No No
## Q98059.fctr Q98078.fctr Q96024.fctr
## 99 Yes Yes No
## 769 Yes No No
## Hhold.fctr .clusterid Hhold.fctr.clusterid D R .entropy .knt
## 1 N 1 N_1 16 3 0.4361623 19
## 2 N 2 N_2 9 1 0.3250830 10
## 3 N 3 N_3 8 0 0.0000000 8
## 4 N 4 N_4 3 1 0.5623351 4
## 5 N 5 N_5 3 1 0.5623351 4
## 6 N 6 N_6 1 3 0.5623351 4
## 7 MKn 1 MKn_1 59 11 0.4349016 70
## 8 MKn 2 MKn_2 21 10 0.6287994 31
## 9 MKn 3 MKn_3 17 5 0.5359599 22
## 10 MKy 1 MKy_1 76 16 0.4620369 92
## 11 MKy 2 MKy_2 55 17 0.5465557 72
## 12 MKy 3 MKy_3 55 10 0.4293230 65
## 13 PKn 1 PKn_1 32 3 0.2925085 35
## 14 PKn 2 PKn_2 13 2 0.3926745 15
## 15 PKy 1 PKy_1 10 1 0.3046361 11
## 16 SKn 1 SKn_1 191 56 0.5352834 247
## 17 SKn 2 SKn_2 134 36 0.5162854 170
## 18 SKy 1 SKy_1 22 5 0.4791656 27
## 19 SKy 2 SKy_2 8 1 0.3488321 9
## 20 SKy 3 SKy_3 6 0 0.0000000 6
## 21 SKy 4 SKy_4 3 1 0.5623351 4
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.4839 (98.0037 pct)"## label step_major step_minor label_minor bgn end
## 1 cluster.data 1 0 0 9.273 29.707
## 2 partition.data.training 2 0 0 29.707 NA
## elapsed
## 1 20.434
## 2 NAStep 2.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk## Loading required package: reshape2## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 3.12 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 3.12 secs"
## [1] "lclgetMatrixSimilarity: duration: 4.624000 secs"## Loading required package: sampling##
## Attaching package: 'sampling'## The following object is masked from 'package:caret':
##
## cluster## Stratum 1
##
## Population total and number of selected units: 40 7
## Stratum 2
##
## Population total and number of selected units: 97 18
## Stratum 3
##
## Population total and number of selected units: 186 37
## Stratum 4
##
## Population total and number of selected units: 45 7
## Stratum 5
##
## Population total and number of selected units: 10 2
## Stratum 6
##
## Population total and number of selected units: 325 74
## Stratum 7
##
## Population total and number of selected units: 39 7
## Stratum 8
##
## Population total and number of selected units: 9 2
## Stratum 9
##
## Population total and number of selected units: 26 5
## Stratum 10
##
## Population total and number of selected units: 43 10
## Stratum 11
##
## Population total and number of selected units: 5 2
## Stratum 12
##
## Population total and number of selected units: 1 1
## Stratum 13
##
## Population total and number of selected units: 92 19
## Stratum 14
##
## Population total and number of selected units: 7 2
## Number of strata 14
## Total number of selected units 193
## [1] "lclgetMatrixSimilarity: duration: 3.086000 secs"
## [1] "lclgetMatrixSimilarity: duration: 1.288000 secs"
## [1] "lclgetMatrixSimilarity: duration: 1.228000 secs"
## [1] "lclgetMatrixSimilarity: duration: 3.624000 secs"
## [1] "Similarity of partitions:"
## cor cosineSmy obs.x obs.y
## 1 0.9999868 0.9499923 OOB Fit
## 2 0.9999870 0.9512085 OOB New
## 3 0.9999873 0.9058551 Fit New## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 18.30 secs"## Party.Democrat Party.Republican Party.NA
## NA NA 223
## Fit 590 142 NA
## OOB 152 41 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.8060109 0.1939891 NA
## OOB 0.7875648 0.2124352 NA
## Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6 SKn 324 93 110 0.44262295 0.48186528
## 2 MKy 182 47 55 0.24863388 0.24352332
## 1 MKn 100 23 26 0.13661202 0.11917098
## 4 PKn 41 9 10 0.05601093 0.04663212
## 3 N 40 9 10 0.05464481 0.04663212
## 7 SKy 37 9 10 0.05054645 0.04663212
## 5 PKy 8 3 2 0.01092896 0.01554404
## .freqRatio.Tst
## 6 0.49327354
## 2 0.24663677
## 1 0.11659193
## 4 0.04484305
## 3 0.04484305
## 7 0.04484305
## 5 0.00896861## [1] "glbObsAll: "## [1] 1148 222## [1] "glbObsTrn: "## [1] 925 222## [1] "glbObsFit: "## [1] 732 221## [1] "glbObsOOB: "## [1] 193 221## [1] "glbObsNew: "## [1] 223 221## [1] "partition.data.training chunk: teardown: elapsed: 19.05 secs"## label step_major step_minor label_minor bgn end
## 2 partition.data.training 2 0 0 29.707 48.832
## 3 select.features 3 0 0 48.833 NA
## elapsed
## 2 19.125
## 3 NAStep 3.0: select features
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## [1] "cor(Q121699.fctr, Q121700.fctr)=0.7060"
## [1] "cor(Party.fctr, Q121699.fctr)=-0.0619"
## [1] "cor(Party.fctr, Q121700.fctr)=-0.0137"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q121700.fctr as highly correlated with Q121699.fctr## cor.y exclude.as.feat cor.y.abs cor.high.X
## YOB 5.385909e-02 1 5.385909e-02 <NA>
## .clusterid 3.185552e-02 1 3.185552e-02 <NA>
## .clusterid.fctr 3.185552e-02 0 3.185552e-02 <NA>
## Gender.fctr 2.915818e-02 0 2.915818e-02 <NA>
## Q108754.fctr 2.873095e-02 0 2.873095e-02 <NA>
## Q108856.fctr 2.598500e-02 0 2.598500e-02 <NA>
## Q120014.fctr 2.540969e-02 0 2.540969e-02 <NA>
## Q115611.fctr 2.414298e-02 0 2.414298e-02 <NA>
## Q102906.fctr 2.383912e-02 0 2.383912e-02 <NA>
## Q101596.fctr 2.350567e-02 0 2.350567e-02 <NA>
## .pos 2.273256e-02 1 2.273256e-02 <NA>
## USER_ID 2.272035e-02 1 2.272035e-02 <NA>
## Q120194.fctr 2.095979e-02 0 2.095979e-02 <NA>
## Hhold.fctr 1.860696e-02 0 1.860696e-02 <NA>
## Q99480.fctr 1.780216e-02 0 1.780216e-02 <NA>
## Q108343.fctr 1.682882e-02 0 1.682882e-02 <NA>
## Q108617.fctr 1.602682e-02 0 1.602682e-02 <NA>
## Q108855.fctr 1.395626e-02 0 1.395626e-02 <NA>
## Q109367.fctr 1.103777e-02 0 1.103777e-02 <NA>
## Q117193.fctr 1.069452e-02 0 1.069452e-02 <NA>
## Q99982.fctr 9.622502e-03 0 9.622502e-03 <NA>
## Q114748.fctr 7.753180e-03 0 7.753180e-03 <NA>
## Q111580.fctr 7.655353e-03 0 7.655353e-03 <NA>
## Q98197.fctr 6.126550e-03 0 6.126550e-03 <NA>
## Q101163.fctr 5.914503e-03 0 5.914503e-03 <NA>
## Q102289.fctr 5.885855e-03 0 5.885855e-03 <NA>
## Q116881.fctr 5.319134e-03 0 5.319134e-03 <NA>
## Q101162.fctr 3.836084e-03 0 3.836084e-03 <NA>
## Q102674.fctr 3.017088e-03 0 3.017088e-03 <NA>
## Q102089.fctr 2.323547e-03 0 2.323547e-03 <NA>
## Q118232.fctr 2.165304e-03 0 2.165304e-03 <NA>
## Q118117.fctr 6.721935e-04 0 6.721935e-04 <NA>
## Q99581.fctr 1.641131e-05 0 1.641131e-05 <NA>
## Q108342.fctr -1.165784e-04 0 1.165784e-04 <NA>
## Q113584.fctr -2.536542e-04 0 2.536542e-04 <NA>
## Edn.fctr -4.631990e-04 0 4.631990e-04 <NA>
## Q113181.fctr -2.987810e-03 0 2.987810e-03 <NA>
## Q115899.fctr -3.391939e-03 0 3.391939e-03 <NA>
## Q122771.fctr -3.593507e-03 0 3.593507e-03 <NA>
## Q106388.fctr -4.089574e-03 0 4.089574e-03 <NA>
## Q113583.fctr -5.119777e-03 0 5.119777e-03 <NA>
## Q119334.fctr -5.832642e-03 0 5.832642e-03 <NA>
## Q105655.fctr -5.967822e-03 0 5.967822e-03 <NA>
## Q115777.fctr -6.671644e-03 0 6.671644e-03 <NA>
## Q98869.fctr -7.300228e-03 0 7.300228e-03 <NA>
## Q115602.fctr -8.572164e-03 0 8.572164e-03 <NA>
## Q107869.fctr -8.710476e-03 0 8.710476e-03 <NA>
## .rnorm -1.022887e-02 0 1.022887e-02 <NA>
## Q120472.fctr -1.117239e-02 0 1.117239e-02 <NA>
## Q100562.fctr -1.304301e-02 0 1.304301e-02 <NA>
## Q115610.fctr -1.308619e-02 0 1.308619e-02 <NA>
## Q121700.fctr -1.373009e-02 0 1.373009e-02 Q121699.fctr
## Q106042.fctr -1.533181e-02 0 1.533181e-02 <NA>
## Q116441.fctr -1.548317e-02 0 1.548317e-02 <NA>
## YOB.Age.dff -1.579002e-02 0 1.579002e-02 <NA>
## Q119650.fctr -1.650551e-02 0 1.650551e-02 <NA>
## Q120978.fctr -1.708157e-02 0 1.708157e-02 <NA>
## Income.fctr -1.805407e-02 0 1.805407e-02 <NA>
## Q99716.fctr -1.819015e-02 0 1.819015e-02 <NA>
## Q102687.fctr -1.939956e-02 0 1.939956e-02 <NA>
## Q107491.fctr -1.953297e-02 0 1.953297e-02 <NA>
## Q100010.fctr -2.019365e-02 0 2.019365e-02 <NA>
## Q112270.fctr -2.229876e-02 0 2.229876e-02 <NA>
## Q123464.fctr -2.303014e-02 0 2.303014e-02 <NA>
## Q104996.fctr -2.394016e-02 0 2.394016e-02 <NA>
## Q116797.fctr -2.401541e-02 0 2.401541e-02 <NA>
## Q116601.fctr -2.424136e-02 0 2.424136e-02 <NA>
## Q116953.fctr -2.434193e-02 0 2.434193e-02 <NA>
## Q110740.fctr -2.599180e-02 0 2.599180e-02 <NA>
## Q103293.fctr -2.609312e-02 0 2.609312e-02 <NA>
## Q122120.fctr -2.635370e-02 0 2.635370e-02 <NA>
## Q108950.fctr -2.796314e-02 0 2.796314e-02 <NA>
## Q100680.fctr -2.839756e-02 0 2.839756e-02 <NA>
## Q122769.fctr -2.867161e-02 0 2.867161e-02 <NA>
## Q106993.fctr -2.914206e-02 0 2.914206e-02 <NA>
## Q111848.fctr -3.061679e-02 0 3.061679e-02 <NA>
## Q121011.fctr -3.123022e-02 0 3.123022e-02 <NA>
## Q115195.fctr -3.162016e-02 0 3.162016e-02 <NA>
## Q120650.fctr -3.168805e-02 0 3.168805e-02 <NA>
## Q96024.fctr -3.182566e-02 0 3.182566e-02 <NA>
## Q112512.fctr -3.209601e-02 0 3.209601e-02 <NA>
## Q118233.fctr -3.272307e-02 0 3.272307e-02 <NA>
## Q116448.fctr -3.325689e-02 0 3.325689e-02 <NA>
## Q106389.fctr -3.366341e-02 0 3.366341e-02 <NA>
## Q118237.fctr -3.406424e-02 0 3.406424e-02 <NA>
## Q124742.fctr -3.410578e-02 0 3.410578e-02 <NA>
## Q111220.fctr -3.435982e-02 0 3.435982e-02 <NA>
## Q117186.fctr -3.488234e-02 0 3.488234e-02 <NA>
## Q106272.fctr -3.593469e-02 0 3.593469e-02 <NA>
## Q98059.fctr -3.688543e-02 0 3.688543e-02 <NA>
## Q120379.fctr -3.777954e-02 0 3.777954e-02 <NA>
## Q105840.fctr -3.782042e-02 0 3.782042e-02 <NA>
## Q114961.fctr -3.846716e-02 0 3.846716e-02 <NA>
## Q98578.fctr -3.905193e-02 0 3.905193e-02 <NA>
## Q122770.fctr -3.924562e-02 0 3.924562e-02 <NA>
## Q106997.fctr -3.925127e-02 0 3.925127e-02 <NA>
## YOB.Age.fctr -4.299538e-02 0 4.299538e-02 <NA>
## Q98078.fctr -4.318029e-02 0 4.318029e-02 <NA>
## Q100689.fctr -4.610720e-02 0 4.610720e-02 <NA>
## Q119851.fctr -4.784957e-02 0 4.784957e-02 <NA>
## Q112478.fctr -4.791303e-02 0 4.791303e-02 <NA>
## Q118892.fctr -4.849461e-02 0 4.849461e-02 <NA>
## Q116197.fctr -4.953483e-02 0 4.953483e-02 <NA>
## Q113992.fctr -5.060894e-02 0 5.060894e-02 <NA>
## Q123621.fctr -5.140758e-02 0 5.140758e-02 <NA>
## Q120012.fctr -5.301143e-02 0 5.301143e-02 <NA>
## Q115390.fctr -5.425424e-02 0 5.425424e-02 <NA>
## Q121699.fctr -6.186040e-02 0 6.186040e-02 <NA>
## Q114517.fctr -6.233932e-02 0 6.233932e-02 <NA>
## Q124122.fctr -6.976947e-02 0 6.976947e-02 <NA>
## Q114386.fctr -7.613008e-02 0 7.613008e-02 <NA>
## Q114152.fctr -7.783674e-02 0 7.783674e-02 <NA>
## Q109244.fctr NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## YOB 1.055556 6.8108108 FALSE FALSE FALSE
## .clusterid 1.631922 0.6486486 FALSE FALSE FALSE
## .clusterid.fctr 1.631922 0.6486486 FALSE FALSE FALSE
## Gender.fctr 1.531680 0.3243243 FALSE FALSE FALSE
## Q108754.fctr 1.830986 0.3243243 FALSE FALSE FALSE
## Q108856.fctr 2.574074 0.3243243 FALSE FALSE FALSE
## Q120014.fctr 1.266447 0.3243243 FALSE FALSE FALSE
## Q115611.fctr 3.698795 0.3243243 FALSE FALSE FALSE
## Q102906.fctr 1.571429 0.3243243 FALSE FALSE FALSE
## Q101596.fctr 2.316514 0.3243243 FALSE FALSE FALSE
## .pos 1.000000 100.0000000 FALSE FALSE FALSE
## USER_ID 1.000000 100.0000000 FALSE FALSE FALSE
## Q120194.fctr 1.694118 0.3243243 FALSE FALSE FALSE
## Hhold.fctr 1.820961 0.7567568 FALSE FALSE FALSE
## Q99480.fctr 2.540284 0.3243243 FALSE FALSE FALSE
## Q108343.fctr 1.331361 0.3243243 FALSE FALSE FALSE
## Q108617.fctr 5.685950 0.3243243 FALSE FALSE FALSE
## Q108855.fctr 1.422713 0.3243243 FALSE FALSE FALSE
## Q109367.fctr 1.912458 0.3243243 FALSE FALSE FALSE
## Q117193.fctr 1.404844 0.3243243 FALSE FALSE FALSE
## Q99982.fctr 1.519298 0.3243243 FALSE FALSE TRUE
## Q114748.fctr 1.256716 0.3243243 FALSE FALSE TRUE
## Q111580.fctr 1.646429 0.3243243 FALSE FALSE TRUE
## Q98197.fctr 2.581731 0.3243243 FALSE FALSE TRUE
## Q101163.fctr 1.098802 0.3243243 FALSE FALSE TRUE
## Q102289.fctr 2.734694 0.3243243 FALSE FALSE TRUE
## Q116881.fctr 2.200873 0.3243243 FALSE FALSE TRUE
## Q101162.fctr 1.716912 0.3243243 FALSE FALSE TRUE
## Q102674.fctr 1.614286 0.3243243 FALSE FALSE TRUE
## Q102089.fctr 1.779851 0.3243243 FALSE FALSE TRUE
## Q118232.fctr 1.185065 0.3243243 FALSE FALSE TRUE
## Q118117.fctr 1.736059 0.3243243 FALSE FALSE TRUE
## Q99581.fctr 3.551913 0.3243243 FALSE FALSE TRUE
## Q108342.fctr 1.656667 0.3243243 FALSE FALSE TRUE
## Q113584.fctr 1.008380 0.3243243 FALSE FALSE TRUE
## Edn.fctr 1.519737 0.8648649 FALSE FALSE TRUE
## Q113181.fctr 2.378995 0.3243243 FALSE FALSE TRUE
## Q115899.fctr 1.135542 0.3243243 FALSE FALSE TRUE
## Q122771.fctr 2.936275 0.3243243 FALSE FALSE TRUE
## Q106388.fctr 2.675000 0.3243243 FALSE FALSE TRUE
## Q113583.fctr 2.387850 0.3243243 FALSE FALSE TRUE
## Q119334.fctr 1.069767 0.3243243 FALSE FALSE TRUE
## Q105655.fctr 1.210826 0.3243243 FALSE FALSE TRUE
## Q115777.fctr 1.436426 0.3243243 FALSE FALSE TRUE
## Q98869.fctr 2.187500 0.3243243 FALSE FALSE TRUE
## Q115602.fctr 2.958974 0.3243243 FALSE FALSE TRUE
## Q107869.fctr 1.021220 0.3243243 FALSE FALSE TRUE
## .rnorm 1.000000 100.0000000 FALSE FALSE FALSE
## Q120472.fctr 1.631579 0.3243243 FALSE FALSE FALSE
## Q100562.fctr 3.216931 0.3243243 FALSE FALSE FALSE
## Q115610.fctr 3.240642 0.3243243 FALSE FALSE FALSE
## Q121700.fctr 3.472826 0.3243243 FALSE FALSE FALSE
## Q106042.fctr 1.235650 0.3243243 FALSE FALSE FALSE
## Q116441.fctr 2.041667 0.3243243 FALSE FALSE FALSE
## YOB.Age.dff 1.027586 1.7297297 FALSE FALSE FALSE
## Q119650.fctr 2.458150 0.3243243 FALSE FALSE FALSE
## Q120978.fctr 1.331148 0.3243243 FALSE FALSE FALSE
## Income.fctr 1.056738 0.7567568 FALSE FALSE FALSE
## Q99716.fctr 3.418478 0.3243243 FALSE FALSE FALSE
## Q102687.fctr 1.125698 0.3243243 FALSE FALSE FALSE
## Q107491.fctr 4.744966 0.3243243 FALSE FALSE FALSE
## Q100010.fctr 3.204420 0.3243243 FALSE FALSE FALSE
## Q112270.fctr 1.675373 0.3243243 FALSE FALSE FALSE
## Q123464.fctr 2.484127 0.3243243 FALSE FALSE FALSE
## Q104996.fctr 1.071038 0.3243243 FALSE FALSE FALSE
## Q116797.fctr 1.715356 0.3243243 FALSE FALSE FALSE
## Q116601.fctr 3.411765 0.3243243 FALSE FALSE FALSE
## Q116953.fctr 2.000000 0.3243243 FALSE FALSE FALSE
## Q110740.fctr 1.245665 0.3243243 FALSE FALSE FALSE
## Q103293.fctr 1.102778 0.3243243 FALSE FALSE FALSE
## Q122120.fctr 2.651961 0.3243243 FALSE FALSE FALSE
## Q108950.fctr 2.028470 0.3243243 FALSE FALSE FALSE
## Q100680.fctr 2.336323 0.3243243 FALSE FALSE FALSE
## Q122769.fctr 1.627376 0.3243243 FALSE FALSE FALSE
## Q106993.fctr 3.842767 0.3243243 FALSE FALSE FALSE
## Q111848.fctr 2.304721 0.3243243 FALSE FALSE FALSE
## Q121011.fctr 1.541958 0.3243243 FALSE FALSE FALSE
## Q115195.fctr 2.173913 0.3243243 FALSE FALSE FALSE
## Q120650.fctr 3.084507 0.3243243 FALSE FALSE FALSE
## Q96024.fctr 1.284810 0.3243243 FALSE FALSE FALSE
## Q112512.fctr 3.346154 0.3243243 FALSE FALSE FALSE
## Q118233.fctr 2.375000 0.3243243 FALSE FALSE FALSE
## Q116448.fctr 1.019337 0.3243243 FALSE FALSE FALSE
## Q106389.fctr 1.122807 0.3243243 FALSE FALSE FALSE
## Q118237.fctr 1.005682 0.3243243 FALSE FALSE FALSE
## Q124742.fctr 1.712230 0.3243243 FALSE FALSE FALSE
## Q111220.fctr 2.500000 0.3243243 FALSE FALSE FALSE
## Q117186.fctr 1.983051 0.3243243 FALSE FALSE FALSE
## Q106272.fctr 1.921875 0.3243243 FALSE FALSE FALSE
## Q98059.fctr 4.217391 0.3243243 FALSE FALSE FALSE
## Q120379.fctr 1.279743 0.3243243 FALSE FALSE FALSE
## Q105840.fctr 1.295031 0.3243243 FALSE FALSE FALSE
## Q114961.fctr 1.095930 0.3243243 FALSE FALSE FALSE
## Q98578.fctr 1.853175 0.3243243 FALSE FALSE FALSE
## Q122770.fctr 1.355049 0.3243243 FALSE FALSE FALSE
## Q106997.fctr 1.095109 0.3243243 FALSE FALSE FALSE
## YOB.Age.fctr 1.100719 0.9729730 FALSE FALSE FALSE
## Q98078.fctr 1.051576 0.3243243 FALSE FALSE FALSE
## Q100689.fctr 1.865672 0.3243243 FALSE FALSE FALSE
## Q119851.fctr 1.059322 0.3243243 FALSE FALSE FALSE
## Q112478.fctr 1.733083 0.3243243 FALSE FALSE FALSE
## Q118892.fctr 2.668317 0.3243243 FALSE FALSE FALSE
## Q116197.fctr 1.708333 0.3243243 FALSE FALSE FALSE
## Q113992.fctr 2.575472 0.3243243 FALSE FALSE FALSE
## Q123621.fctr 1.002985 0.3243243 FALSE FALSE FALSE
## Q120012.fctr 1.019943 0.3243243 FALSE FALSE FALSE
## Q115390.fctr 1.543478 0.3243243 FALSE FALSE FALSE
## Q121699.fctr 3.090909 0.3243243 FALSE FALSE FALSE
## Q114517.fctr 2.334821 0.3243243 FALSE FALSE FALSE
## Q124122.fctr 1.375912 0.3243243 FALSE FALSE FALSE
## Q114386.fctr 1.211310 0.3243243 FALSE FALSE FALSE
## Q114152.fctr 2.319444 0.3243243 FALSE FALSE FALSE
## Q109244.fctr 0.000000 0.1081081 TRUE TRUE NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr NA 0 NA <NA> 0
## percentUnique zeroVar nzv is.cor.y.abs.low
## Q109244.fctr 0.1081081 TRUE TRUE NA## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in : "
## YOB Party.fctr
## 48 223
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff
## 49
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## Gender Income HouseholdStatus EducationLevel
## 9 163 59 136
## Party Q124742 Q124122 Q123464
## NA 589 337 309
## Q123621 Q122769 Q122770 Q122771
## 312 279 245 247
## Q122120 Q121699 Q121700 Q120978
## 248 224 222 257
## Q121011 Q120379 Q120650 Q120472
## 238 269 261 280
## Q120194 Q120012 Q120014 Q119334
## 292 265 289 257
## Q119851 Q119650 Q118892 Q118117
## 245 274 227 228
## Q118232 Q118233 Q118237 Q117186
## 311 268 264 283
## Q117193 Q116797 Q116881 Q116953
## 276 242 276 279
## Q116601 Q116441 Q116448 Q116197
## 225 239 243 248
## Q115602 Q115777 Q115610 Q115611
## 236 264 230 202
## Q115899 Q115390 Q114961 Q114748
## 260 279 244 207
## Q115195 Q114517 Q114386 Q113992
## 242 216 214 206
## Q114152 Q113583 Q113584 Q113181
## 261 235 241 217
## Q112478 Q112512 Q112270 Q111848
## 229 212 249 179
## Q111580 Q111220 Q110740 Q109367
## 219 191 173 73
## Q108950 Q109244 Q108855 Q108617
## 91 0 191 143
## Q108856 Q108754 Q108342 Q108343
## 183 148 157 162
## Q107869 Q107491 Q106993 Q106997
## 196 176 187 193
## Q106272 Q106388 Q106389 Q106042
## 212 228 236 218
## Q105840 Q105655 Q104996 Q103293
## 222 177 201 201
## Q102906 Q102674 Q102687 Q102289
## 231 237 199 227
## Q102089 Q101162 Q101163 Q101596
## 222 224 271 243
## Q100689 Q100680 Q100562 Q99982
## 185 216 226 243
## Q100010 Q99716 Q99581 Q99480
## 210 219 214 206
## Q98869 Q98578 Q98059 Q98078
## 254 252 195 251
## Q98197 Q96024 .lcn
## 224 243 223## [1] "glb_feats_df:"## [1] 113 12## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID 0.02272035 TRUE 0.02272035 <NA>
## Party.fctr Party.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID <NA> NA FALSE TRUE
## Party.fctr <NA> NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 3 select.features 3 0 0 48.833 51.324 2.491
## 4 fit.models 4 0 0 51.324 NA NAStep 4.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 51.853 NA NA# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 51.853 51.886
## 2 fit.models_0_MFO 1 1 myMFO_classfr 51.886 NA
## elapsed
## 1 0.033
## 2 NA
## [1] "myfit_mdl: enter: 0.002000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.454000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
## D R
## 0.8060109 0.1939891
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.890000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used## [1] "myfit_mdl: train diagnostics complete: 0.894000 secs"## Loading required namespace: pROC## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## D R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.8060109 0
## 6 0.25 0 0.8060109 0
## 7 0.30 0 0.8060109 0
## 8 0.35 0 0.8060109 0
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.7875648 0
## 6 0.25 0 0.7875648 0
## 7 0.30 0 0.7875648 0
## 8 0.35 0 0.7875648 0
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 7.606000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.429
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.003 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.8060109
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7754723 0.8340591 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.7875648
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7230441 0.8430301 0
## [1] "in MFO.Classifier$prob"
## D R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "myfit_mdl: exit: 7.649000 secs"
## label step_major step_minor label_minor bgn end
## 2 fit.models_0_MFO 1 1 myMFO_classfr 51.886 59.54
## 3 fit.models_0_Random 1 2 myrandom_classfr 59.541 NA
## elapsed
## 2 7.654
## 3 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.404000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.675000 secs"
## parameter
## 1 none
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.677000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "in Random.Classifier$prob"
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 7.301000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.267 0.001 0.5244092
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8305085 0.2183099 0.4867868 0.85
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060109 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.4922978 0.7894737
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.195122 0.5012035 0.85 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB
## 1 0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 7.532000 secs"# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 59.541 67.085 7.544
## 4 67.086 NA NAret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: Q114152.fctr,Q114386.fctr"
## [1] "myfit_mdl: setup complete: 0.697000 secs"## Loading required package: glmnet## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loaded glmnet 2.0-5## Fitting alpha = 0.1, lambda = 0.000641 on full training set
## [1] "myfit_mdl: train complete: 1.491000 secs"
## alpha lambda
## 1 0.1 0.0006408924
## Length Class Mode
## a0 48 -none- numeric
## beta 192 dgCMatrix S4
## df 48 -none- numeric
## dim 2 -none- numeric
## lambda 48 -none- numeric
## dev.ratio 48 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 4 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Q114152.fctrNo Q114152.fctrYes
## -1.13166150 0.01441013 -0.47004128
## Q114386.fctrMysterious Q114386.fctrTMI
## -0.30122736 -0.23102023
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "Q114152.fctrNo"
## [3] "Q114152.fctrYes" "Q114386.fctrMysterious"
## [5] "Q114386.fctrTMI"
## [1] "myfit_mdl: train diagnostics complete: 1.598000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 6 0.25 0 0.8060109 0
## 7 0.30 0 0.8060109 0
## 8 0.35 0 0.8060109 0
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 6 0.25 0 0.7875648 0
## 7 0.30 0 0.7875648 0
## 8 0.35 0 0.7875648 0
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 8.050000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q114152.fctr,Q114386.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.788 0.015 0.5
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 1 0 0.5625269 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060109 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.5 1
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0 0.5478979 0.5 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 8.098000 secs"if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: Q114152.fctr,Q114386.fctr"
## [1] "myfit_mdl: setup complete: 0.696000 secs"## Loading required package: rpart## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.276000 secs"
## cp
## 1 0## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 732
##
## CP nsplit rel error
## 1 0 0 1
##
## Node number 1: 732 observations
## predicted class=D expected loss=0.1939891 P(node) =1
## class counts: 590 142
## probabilities: 0.806 0.194
##
## n= 732
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 732 142 D (0.8060109 0.1939891) *
## [1] "myfit_mdl: train diagnostics complete: 2.429000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.8060109 0
## 6 0.25 0 0.8060109 0
## 7 0.30 0 0.8060109 0
## 8 0.35 0 0.8060109 0
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 5 0.20 0 0.7875648 0
## 6 0.25 0 0.7875648 0
## 7 0.30 0 0.7875648 0
## 8 0.35 0 0.7875648 0
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 8.951000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q114152.fctr,Q114386.fctr 1
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.576 0.008 0.5
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 1 0 0.5 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060121 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.5 1
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0 0.5 0.5 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.001793698 0
## [1] "myfit_mdl: exit: 8.995000 secs"if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
} ## label step_major step_minor label_minor
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## bgn end elapsed
## 4 67.086 84.218 17.132
## 5 84.219 NA NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] " indepVar: Q114152.fctr,Q114386.fctr,Q114152.fctr:Q121699.fctr"
## [1] "myfit_mdl: setup complete: 0.704000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0211 on full training set
## [1] "myfit_mdl: train complete: 2.433000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 56 -none- numeric
## beta 560 dgCMatrix S4
## df 56 -none- numeric
## dim 2 -none- numeric
## lambda 56 -none- numeric
## dev.ratio 56 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 10 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Q114152.fctrNA:Q121699.fctrNo
## -1.3660730 0.8520755
## Q114152.fctrNA:Q121699.fctrYes Q114152.fctrYes:Q121699.fctrYes
## -0.1969547 -0.6620578
## [1] "max lambda < lambdaOpt:"
## (Intercept) Q114152.fctrNA:Q121699.fctrNo
## -1.362457755 0.874180212
## Q114152.fctrYes:Q121699.fctrNo Q114152.fctrNA:Q121699.fctrYes
## 0.009467681 -0.225224906
## Q114152.fctrYes:Q121699.fctrYes
## -0.693412776
## [1] "myfit_mdl: train diagnostics complete: 3.041000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 9 0.40 0 0.8060109 0
## 10 0.45 0 0.8060109 0
## 11 0.50 0 0.8060109 0
## 12 0.55 0 0.8060109 0
## 13 0.60 0 0.8060109 0
## 14 0.65 0 0.8060109 0
## 15 0.70 0 0.8060109 0
## 16 0.75 0 0.8060109 0
## 17 0.80 0 0.8060109 0
## 18 0.85 0 0.8060109 0
## 19 0.90 0 0.8060109 0
## 20 0.95 0 0.8060109 0
## 21 1.00 0 0.8060109 0
## Prediction
## Reference D R
## D 590 0
## R 142 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.060109e-01 0.000000e+00 7.754723e-01 8.340591e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 5.224287e-01 2.652612e-32
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy g.score
## 9 0.40 0 0.7875648 0
## 10 0.45 0 0.7875648 0
## 11 0.50 0 0.7875648 0
## 12 0.55 0 0.7875648 0
## 13 0.60 0 0.7875648 0
## 14 0.65 0 0.7875648 0
## 15 0.70 0 0.7875648 0
## 16 0.75 0 0.7875648 0
## 17 0.80 0 0.7875648 0
## 18 0.85 0 0.7875648 0
## 19 0.90 0 0.7875648 0
## 20 0.95 0 0.7875648 0
## 21 1.00 0 0.7875648 0
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 9.529000 secs"
## id
## 1 Interact.High.cor.Y##rcv#glmnet
## feats max.nTuningRuns
## 1 Q114152.fctr,Q114386.fctr,Q114152.fctr:Q121699.fctr 20
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.724 0.017 0.5
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 1 0 0.5972487 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0 0.8060121 0.7754723
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8340591 0 0.5 1
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0 0.4963094 0.5 0
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7875648 0.7230441 0.8430301
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.001881246 0
## [1] "myfit_mdl: exit: 9.599000 secs"# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## bgn end elapsed
## 5 84.219 93.83 9.611
## 6 93.830 NA NAindepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.blockParallel = glbMdlSequential,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.739000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0223 on full training set
## [1] "myfit_mdl: train complete: 27.659000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 84 -none- numeric
## beta 22848 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 272 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.291984516 -0.359967281
## Edn.fctr^5 Hhold.fctrPKn
## 0.229127626 -0.048297383
## Q100562.fctrNo Q100680.fctrNo
## 0.103164723 0.011944055
## Q101596.fctrNo Q112478.fctrNo
## 0.014216556 0.123652868
## Q114152.fctrYes Q115610.fctrNo
## -0.154057660 0.156094317
## Q116197.fctrA.M. Q116953.fctrNo
## 0.093159631 0.081367555
## Q117193.fctrStandard hours Q118232.fctrId
## 0.008141328 -0.261912459
## Q119851.fctrNo Q120194.fctrStudy first
## 0.012983392 -0.138311662
## Q120194.fctrTry first Q98197.fctrNo
## 0.133738104 -0.308007491
## Q98578.fctrYes Hhold.fctrN:.clusterid.fctr6
## -0.061503513 1.919850547
## YOB.Age.fctr(15,20]:YOB.Age.dff
## 0.059071038
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.320911682 -0.421891827
## Edn.fctr^5 Hhold.fctrPKn
## 0.236006615 -0.071954796
## Q100562.fctrNo Q100680.fctrNo
## 0.147431816 0.038869951
## Q101596.fctrNo Q106042.fctrNo
## 0.047949711 0.004979051
## Q106272.fctrNo Q112478.fctrNo
## 0.011597674 0.153223876
## Q114152.fctrYes Q114517.fctrNo
## -0.189846153 0.015395017
## Q114517.fctrYes Q115390.fctrNo
## -0.017066286 0.026580076
## Q115610.fctrNo Q116197.fctrA.M.
## 0.193618993 0.131674135
## Q116953.fctrNo Q117186.fctrCool headed
## 0.106235592 0.011479302
## Q117193.fctrStandard hours Q118232.fctrId
## 0.030159368 -0.306219222
## Q119851.fctrNo Q120194.fctrStudy first
## 0.044376260 -0.181328326
## Q120194.fctrTry first Q124742.fctrNo
## 0.128292688 -0.024955507
## Q98197.fctrNo Q98578.fctrYes
## -0.368031159 -0.101027779
## YOB.Age.fctr^7 Hhold.fctrMKn:.clusterid.fctr2
## 0.043516626 0.057488010
## Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff
## 2.075389191 0.070473734
## [1] "myfit_mdl: train diagnostics complete: 28.305000 secs"
## Prediction
## Reference D R
## D 589 1
## R 132 10
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.183060e-01 1.057756e-01 7.884255e-01 8.455940e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 2.144711e-01 1.795460e-29
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 37.529000 secs"
## id
## 1 Low.cor.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 26.852 9.473
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5105634 1 0.02112676 0.7311112
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.130719 0.8069191
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7884255 0.845594 0.01298693
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.4967105 0.9934211 0 0.546534
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.65 0 0.7875648
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7230441 0.8430301 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.003930609 0.02570105
## [1] "myfit_mdl: exit: 37.731000 secs"fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 6 fit.models_0_Low.cor.X 1 5 glmnet 93.830 131.592
## 7 fit.models_0_end 1 6 teardown 131.593 NA
## elapsed
## 6 37.763
## 7 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 4 fit.models 4 0 0 51.324 131.606 80.282
## 5 fit.models 4 1 1 131.607 NA NAif (!is.null(glbChunks$first) && (glbChunks$first == "fit.models_1") &&
(is.null(knitr::opts_current$get(name = 'label')))) # not knitting
myloadChunk(glbChunks$inpFilePathName,
keepSpec = c("glbMdlFamilies","glbMdlSequential","glbMdlPreprocMethods",
"glbMdlTuneParams","glbMdlSelId","glbMdlEnsemble"),
dropSpec = c(NULL))## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 136.172 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 136.172 136.185
## 2 fit.models_1_All.X 1 1 setup 136.186 NA
## elapsed
## 1 0.014
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 136.186 136.193
## 3 fit.models_1_All.X 1 2 glmnet 136.194 NA
## elapsed
## 2 0.008
## 3 NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 1.029000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.04 on full training set
## [1] "myfit_mdl: train complete: 23.583000 secs"
## Length Class Mode
## a0 100 -none- numeric
## beta 27400 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 274 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.285449323 -0.304102172
## Edn.fctr^5 Hhold.fctrPKn
## 0.214607283 -0.055097266
## Q100562.fctrNo Q100680.fctrNo
## 0.085446768 0.002204116
## Q101596.fctrNo Q112478.fctrNo
## 0.002890755 0.105009913
## Q114152.fctrYes Q115610.fctrNo
## -0.133752090 0.130323349
## Q116197.fctrA.M. Q116953.fctrNo
## 0.073139264 0.072668101
## Q117193.fctrStandard hours Q118232.fctrId
## 0.010279080 -0.224580754
## Q119851.fctrNo Q120194.fctrStudy first
## 0.008937906 -0.116072075
## Q120194.fctrTry first Q121699.fctrNo
## 0.136945422 0.081298033
## Q121699.fctrYes Q98197.fctrNo
## -0.060852588 -0.250727021
## Q98578.fctrYes YOB.Age.fctr^7
## -0.063253634 0.001048372
## Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff
## 1.773160941 0.040871999
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.3026929694 -0.3600681029
## Edn.fctr^5 Hhold.fctrPKn
## 0.2257899080 -0.0791915425
## Q100562.fctrNo Q100680.fctrNo
## 0.1226209054 0.0283324887
## Q101596.fctrNo Q106272.fctrNo
## 0.0339588645 0.0042975876
## Q112478.fctrNo Q114152.fctrYes
## 0.1327317705 -0.1665812702
## Q114517.fctrNo Q114517.fctrYes
## 0.0046106768 -0.0127125638
## Q115390.fctrNo Q115610.fctrNo
## 0.0280552504 0.1660103263
## Q116197.fctrA.M. Q116953.fctrNo
## 0.1066048589 0.0967694780
## Q117186.fctrCool headed Q117193.fctrStandard hours
## 0.0090020902 0.0313391640
## Q118232.fctrId Q119851.fctrNo
## -0.2629854510 0.0365066033
## Q120194.fctrStudy first Q120194.fctrTry first
## -0.1400377316 0.1488889687
## Q121699.fctrNo Q121699.fctrYes
## 0.0746429895 -0.0876316937
## Q124742.fctrNo Q98197.fctrNo
## -0.0172268361 -0.2980400605
## Q98197.fctrYes Q98578.fctrYes
## 0.0006860013 -0.0999115235
## YOB.Age.fctr^7 Hhold.fctrMKn:.clusterid.fctr2
## 0.0438543597 0.0212298846
## Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff
## 1.9334073871 0.0494671429
## [1] "myfit_mdl: train diagnostics complete: 24.215000 secs"
## Prediction
## Reference D R
## D 558 32
## R 103 39
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.155738e-01 2.720549e-01 7.855431e-01 8.430347e-01 8.060109e-01
## AccuracyPValue McnemarPValue
## 2.737535e-01 1.694856e-09
## Prediction
## Reference D R
## D 152 0
## R 41 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.875648e-01 0.000000e+00 7.230441e-01 8.430301e-01 7.875648e-01
## AccuracyPValue McnemarPValue
## 5.417029e-01 4.185437e-10
## [1] "myfit_mdl: predict complete: 33.407000 secs"
## id
## 1 All.X##rcv#glmnet
## feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 22.489 9.096
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5105634 1 0.02112676 0.733027
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.25 0.3661972 0.8069228
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7855431 0.8430347 0.01021363
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.4967105 0.9934211 0 0.5511874
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0 0.7875648
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7230441 0.8430301 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.003715012 0.02424578
## [1] "myfit_mdl: exit: 33.608000 secs"## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 136.194 169.819
## 4 fit.models_1_preProc 1 3 preProc 169.820 NA
## elapsed
## 3 33.625
## 4 NA## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet 0.7875648 0.5511874
## Max.cor.Y.rcv.1X1###glmnet 0.7875648 0.5478979
## Low.cor.X##rcv#glmnet 0.7875648 0.5465340
## Random###myrandom_classfr 0.7875648 0.5012035
## MFO###myMFO_classfr 0.7875648 0.5000000
## Max.cor.Y##rcv#rpart 0.7875648 0.5000000
## Interact.High.cor.Y##rcv#glmnet 0.7875648 0.4963094
## max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet 0.4967105 22.489
## Max.cor.Y.rcv.1X1###glmnet 0.5000000 0.788
## Low.cor.X##rcv#glmnet 0.4967105 26.852
## Random###myrandom_classfr 0.4922978 0.267
## MFO###myMFO_classfr 0.5000000 0.429
## Max.cor.Y##rcv#rpart 0.5000000 1.576
## Interact.High.cor.Y##rcv#glmnet 0.5000000 1.724
## max.Accuracy.fit
## All.X##rcv#glmnet 0.8069228
## Max.cor.Y.rcv.1X1###glmnet 0.8060109
## Low.cor.X##rcv#glmnet 0.8069191
## Random###myrandom_classfr 0.8060109
## MFO###myMFO_classfr 0.8060109
## Max.cor.Y##rcv#rpart 0.8060121
## Interact.High.cor.Y##rcv#glmnet 0.8060121## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 169.820 171.461
## 5 fit.models_1_end 1 4 teardown 171.462 NA
## elapsed
## 4 1.641
## 5 NA## label step_major step_minor label_minor bgn end elapsed
## 5 fit.models 4 1 1 131.607 171.47 39.863
## 6 fit.models 4 2 2 171.471 NA NA## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 172.591 NA NA## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"## Warning: Removed 4 rows containing missing values (geom_errorbar).## quartz_off_screen
## 2## Warning: Removed 4 rows containing missing values (geom_errorbar).
## id max.Accuracy.OOB max.AUCROCR.OOB
## 7 All.X##rcv#glmnet 0.7875648 0.5511874
## 3 Max.cor.Y.rcv.1X1###glmnet 0.7875648 0.5478979
## 6 Low.cor.X##rcv#glmnet 0.7875648 0.5465340
## 2 Random###myrandom_classfr 0.7875648 0.5012035
## 1 MFO###myMFO_classfr 0.7875648 0.5000000
## 4 Max.cor.Y##rcv#rpart 0.7875648 0.5000000
## 5 Interact.High.cor.Y##rcv#glmnet 0.7875648 0.4963094
## max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 7 0.4967105 22.489 0.8069228
## 3 0.5000000 0.788 0.8060109
## 6 0.4967105 26.852 0.8069191
## 2 0.4922978 0.267 0.8060109
## 1 0.5000000 0.429 0.8060109
## 4 0.5000000 1.576 0.8060121
## 5 0.5000000 1.724 0.8060121
## opt.prob.threshold.fit opt.prob.threshold.OOB
## 7 0.25 0.60
## 3 0.50 0.50
## 6 0.30 0.65
## 2 0.85 0.85
## 1 0.50 0.50
## 4 0.50 0.50
## 5 0.50 0.50## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything -
## max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7ff32313ce98>## [1] "Best model id: All.X##rcv#glmnet"## glmnet
##
## 732 samples
## 108 predictors
## 2 classes: 'D', 'R'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times)
## Summary of sample sizes: 488, 488, 488, 489, 488, 487, ...
## Resampling results across tuning parameters:
##
## alpha lambda Accuracy Kappa
## 0.325 0.001034113 0.6780386 0.005105574
## 0.325 0.004799925 0.7163012 0.028942387
## 0.325 0.022279280 0.7750316 0.009287560
## 0.325 0.040000000 0.8014509 0.010640123
## 0.325 0.060000000 0.8064656 0.009310908
## 0.550 0.001034113 0.6780386 0.004857866
## 0.550 0.004799925 0.7249608 0.029862380
## 0.550 0.022279280 0.7946202 0.013337849
## 0.550 0.040000000 0.8069228 0.010213626
## 0.550 0.060000000 0.8060121 0.000000000
## 0.775 0.001034113 0.6794047 0.004871084
## 0.775 0.004799925 0.7367987 0.038752931
## 0.775 0.022279280 0.8028188 0.004958124
## 0.775 0.040000000 0.8069228 0.007440574
## 0.775 0.060000000 0.8060121 0.000000000
## 0.900 0.001034113 0.6771334 0.005132744
## 0.900 0.004799925 0.7358917 0.034105218
## 0.900 0.022279280 0.8055511 0.010332112
## 0.900 0.040000000 0.8060121 0.000000000
## 0.900 0.060000000 0.8060121 0.000000000
## 1.000 0.001034113 0.6798694 0.002621383
## 1.000 0.004799925 0.7427204 0.043018416
## 1.000 0.022279280 0.8069191 0.012986933
## 1.000 0.040000000 0.8060121 0.000000000
## 1.000 0.060000000 0.8060121 0.000000000
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.55 and lambda = 0.04.## [1] "All.X##rcv#glmnet fit prediction diagnostics:"## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"## All.X..rcv.glmnet.imp imp
## Hhold.fctrN:.clusterid.fctr6 1.000000e+02 1.000000e+02
## Edn.fctr.Q 1.745373e+01 1.745373e+01
## Q98197.fctrNo 1.440277e+01 1.440277e+01
## Q118232.fctrId 1.285848e+01 1.285848e+01
## Edn.fctr^5 1.201560e+01 1.201560e+01
## Q114152.fctrYes 7.764113e+00 7.764113e+00
## Q120194.fctrTry first 7.718628e+00 7.718628e+00
## Q115610.fctrNo 7.604491e+00 7.604491e+00
## Q120194.fctrStudy first 6.689618e+00 6.689618e+00
## Q112478.fctrNo 6.116418e+00 6.116418e+00
## Q100562.fctrNo 5.132660e+00 5.132660e+00
## Q121699.fctrNo 4.435750e+00 4.435750e+00
## Q116197.fctrA.M. 4.410901e+00 4.410901e+00
## Q116953.fctrNo 4.285022e+00 4.285022e+00
## Q98578.fctrYes 3.896913e+00 3.896913e+00
## Q121699.fctrYes 3.658572e+00 3.658572e+00
## Hhold.fctrPKn 3.310930e+00 3.310930e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff 2.357253e+00 2.357253e+00
## Q117193.fctrStandard hours 7.941692e-01 7.941692e-01
## Q119851.fctrNo 7.891620e-01 7.891620e-01
## YOB.Age.fctr^7 5.141455e-01 5.141455e-01
## Q101596.fctrNo 4.912269e-01 4.912269e-01
## Q100680.fctrNo 4.005389e-01 4.005389e-01
## Q115390.fctrNo 2.988845e-01 2.988845e-01
## Hhold.fctrMKn:.clusterid.fctr2 2.261710e-01 2.261710e-01
## Q124742.fctrNo 1.835248e-01 1.835248e-01
## Q114517.fctrYes 1.354323e-01 1.354323e-01
## Q117186.fctrCool headed 9.590309e-02 9.590309e-02
## Q114517.fctrNo 4.911950e-02 4.911950e-02
## Q106272.fctrNo 4.578403e-02 4.578403e-02
## Q98197.fctrYes 7.308264e-03 7.308264e-03
## .rnorm 0.000000e+00 0.000000e+00
## Edn.fctr.L 0.000000e+00 0.000000e+00
## Edn.fctr.C 0.000000e+00 0.000000e+00
## Edn.fctr^4 0.000000e+00 0.000000e+00
## Edn.fctr^6 0.000000e+00 0.000000e+00
## Edn.fctr^7 0.000000e+00 0.000000e+00
## Gender.fctrF 0.000000e+00 0.000000e+00
## Gender.fctrM 0.000000e+00 0.000000e+00
## Hhold.fctrMKn 0.000000e+00 0.000000e+00
## Hhold.fctrMKy 0.000000e+00 0.000000e+00
## Hhold.fctrPKy 0.000000e+00 0.000000e+00
## Hhold.fctrSKn 0.000000e+00 0.000000e+00
## Hhold.fctrSKy 0.000000e+00 0.000000e+00
## Income.fctr.L 0.000000e+00 0.000000e+00
## Income.fctr.Q 0.000000e+00 0.000000e+00
## Income.fctr.C 0.000000e+00 0.000000e+00
## Income.fctr^4 0.000000e+00 0.000000e+00
## Income.fctr^5 0.000000e+00 0.000000e+00
## Income.fctr^6 0.000000e+00 0.000000e+00
## Q100010.fctrNo 0.000000e+00 0.000000e+00
## Q100010.fctrYes 0.000000e+00 0.000000e+00
## Q100562.fctrYes 0.000000e+00 0.000000e+00
## Q100680.fctrYes 0.000000e+00 0.000000e+00
## Q100689.fctrNo 0.000000e+00 0.000000e+00
## Q100689.fctrYes 0.000000e+00 0.000000e+00
## Q101162.fctrOptimist 0.000000e+00 0.000000e+00
## Q101162.fctrPessimist 0.000000e+00 0.000000e+00
## Q101163.fctrDad 0.000000e+00 0.000000e+00
## Q101163.fctrMom 0.000000e+00 0.000000e+00
## Q101596.fctrYes 0.000000e+00 0.000000e+00
## Q102089.fctrOwn 0.000000e+00 0.000000e+00
## Q102089.fctrRent 0.000000e+00 0.000000e+00
## Q102289.fctrNo 0.000000e+00 0.000000e+00
## Q102289.fctrYes 0.000000e+00 0.000000e+00
## Q102674.fctrNo 0.000000e+00 0.000000e+00
## Q102674.fctrYes 0.000000e+00 0.000000e+00
## Q102687.fctrNo 0.000000e+00 0.000000e+00
## Q102687.fctrYes 0.000000e+00 0.000000e+00
## Q102906.fctrNo 0.000000e+00 0.000000e+00
## Q102906.fctrYes 0.000000e+00 0.000000e+00
## Q103293.fctrNo 0.000000e+00 0.000000e+00
## Q103293.fctrYes 0.000000e+00 0.000000e+00
## Q104996.fctrNo 0.000000e+00 0.000000e+00
## Q104996.fctrYes 0.000000e+00 0.000000e+00
## Q105655.fctrNo 0.000000e+00 0.000000e+00
## Q105655.fctrYes 0.000000e+00 0.000000e+00
## Q105840.fctrNo 0.000000e+00 0.000000e+00
## Q105840.fctrYes 0.000000e+00 0.000000e+00
## Q106042.fctrNo 0.000000e+00 0.000000e+00
## Q106042.fctrYes 0.000000e+00 0.000000e+00
## Q106272.fctrYes 0.000000e+00 0.000000e+00
## Q106388.fctrNo 0.000000e+00 0.000000e+00
## Q106388.fctrYes 0.000000e+00 0.000000e+00
## Q106389.fctrNo 0.000000e+00 0.000000e+00
## Q106389.fctrYes 0.000000e+00 0.000000e+00
## Q106993.fctrNo 0.000000e+00 0.000000e+00
## Q106993.fctrYes 0.000000e+00 0.000000e+00
## Q106997.fctrGr 0.000000e+00 0.000000e+00
## Q106997.fctrYy 0.000000e+00 0.000000e+00
## Q107491.fctrNo 0.000000e+00 0.000000e+00
## Q107491.fctrYes 0.000000e+00 0.000000e+00
## Q107869.fctrNo 0.000000e+00 0.000000e+00
## Q107869.fctrYes 0.000000e+00 0.000000e+00
## Q108342.fctrIn-person 0.000000e+00 0.000000e+00
## Q108342.fctrOnline 0.000000e+00 0.000000e+00
## Q108343.fctrNo 0.000000e+00 0.000000e+00
## Q108343.fctrYes 0.000000e+00 0.000000e+00
## Q108617.fctrNo 0.000000e+00 0.000000e+00
## Q108617.fctrYes 0.000000e+00 0.000000e+00
## Q108754.fctrNo 0.000000e+00 0.000000e+00
## Q108754.fctrYes 0.000000e+00 0.000000e+00
## Q108855.fctrUmm... 0.000000e+00 0.000000e+00
## Q108855.fctrYes! 0.000000e+00 0.000000e+00
## Q108856.fctrSocialize 0.000000e+00 0.000000e+00
## Q108856.fctrSpace 0.000000e+00 0.000000e+00
## Q108950.fctrCautious 0.000000e+00 0.000000e+00
## Q108950.fctrRisk-friendly 0.000000e+00 0.000000e+00
## Q109367.fctrNo 0.000000e+00 0.000000e+00
## Q109367.fctrYes 0.000000e+00 0.000000e+00
## Q110740.fctrMac 0.000000e+00 0.000000e+00
## Q110740.fctrPC 0.000000e+00 0.000000e+00
## Q111220.fctrNo 0.000000e+00 0.000000e+00
## Q111220.fctrYes 0.000000e+00 0.000000e+00
## Q111580.fctrDemanding 0.000000e+00 0.000000e+00
## Q111580.fctrSupportive 0.000000e+00 0.000000e+00
## Q111848.fctrNo 0.000000e+00 0.000000e+00
## Q111848.fctrYes 0.000000e+00 0.000000e+00
## Q112270.fctrNo 0.000000e+00 0.000000e+00
## Q112270.fctrYes 0.000000e+00 0.000000e+00
## Q112478.fctrYes 0.000000e+00 0.000000e+00
## Q112512.fctrNo 0.000000e+00 0.000000e+00
## Q112512.fctrYes 0.000000e+00 0.000000e+00
## Q113181.fctrNo 0.000000e+00 0.000000e+00
## Q113181.fctrYes 0.000000e+00 0.000000e+00
## Q113583.fctrTalk 0.000000e+00 0.000000e+00
## Q113583.fctrTunes 0.000000e+00 0.000000e+00
## Q113584.fctrPeople 0.000000e+00 0.000000e+00
## Q113584.fctrTechnology 0.000000e+00 0.000000e+00
## Q113992.fctrNo 0.000000e+00 0.000000e+00
## Q113992.fctrYes 0.000000e+00 0.000000e+00
## Q114152.fctrNo 0.000000e+00 0.000000e+00
## Q114386.fctrMysterious 0.000000e+00 0.000000e+00
## Q114386.fctrTMI 0.000000e+00 0.000000e+00
## Q114748.fctrNo 0.000000e+00 0.000000e+00
## Q114748.fctrYes 0.000000e+00 0.000000e+00
## Q114961.fctrNo 0.000000e+00 0.000000e+00
## Q114961.fctrYes 0.000000e+00 0.000000e+00
## Q115195.fctrNo 0.000000e+00 0.000000e+00
## Q115195.fctrYes 0.000000e+00 0.000000e+00
## Q115390.fctrYes 0.000000e+00 0.000000e+00
## Q115602.fctrNo 0.000000e+00 0.000000e+00
## Q115602.fctrYes 0.000000e+00 0.000000e+00
## Q115610.fctrYes 0.000000e+00 0.000000e+00
## Q115611.fctrNo 0.000000e+00 0.000000e+00
## Q115611.fctrYes 0.000000e+00 0.000000e+00
## Q115777.fctrEnd 0.000000e+00 0.000000e+00
## Q115777.fctrStart 0.000000e+00 0.000000e+00
## Q115899.fctrCs 0.000000e+00 0.000000e+00
## Q115899.fctrMe 0.000000e+00 0.000000e+00
## Q116197.fctrP.M. 0.000000e+00 0.000000e+00
## Q116441.fctrNo 0.000000e+00 0.000000e+00
## Q116441.fctrYes 0.000000e+00 0.000000e+00
## Q116448.fctrNo 0.000000e+00 0.000000e+00
## Q116448.fctrYes 0.000000e+00 0.000000e+00
## Q116601.fctrNo 0.000000e+00 0.000000e+00
## Q116601.fctrYes 0.000000e+00 0.000000e+00
## Q116797.fctrNo 0.000000e+00 0.000000e+00
## Q116797.fctrYes 0.000000e+00 0.000000e+00
## Q116881.fctrHappy 0.000000e+00 0.000000e+00
## Q116881.fctrRight 0.000000e+00 0.000000e+00
## Q116953.fctrYes 0.000000e+00 0.000000e+00
## Q117186.fctrHot headed 0.000000e+00 0.000000e+00
## Q117193.fctrOdd hours 0.000000e+00 0.000000e+00
## Q118117.fctrNo 0.000000e+00 0.000000e+00
## Q118117.fctrYes 0.000000e+00 0.000000e+00
## Q118232.fctrPr 0.000000e+00 0.000000e+00
## Q118233.fctrNo 0.000000e+00 0.000000e+00
## Q118233.fctrYes 0.000000e+00 0.000000e+00
## Q118237.fctrNo 0.000000e+00 0.000000e+00
## Q118237.fctrYes 0.000000e+00 0.000000e+00
## Q118892.fctrNo 0.000000e+00 0.000000e+00
## Q118892.fctrYes 0.000000e+00 0.000000e+00
## Q119334.fctrNo 0.000000e+00 0.000000e+00
## Q119334.fctrYes 0.000000e+00 0.000000e+00
## Q119650.fctrGiving 0.000000e+00 0.000000e+00
## Q119650.fctrReceiving 0.000000e+00 0.000000e+00
## Q119851.fctrYes 0.000000e+00 0.000000e+00
## Q120012.fctrNo 0.000000e+00 0.000000e+00
## Q120012.fctrYes 0.000000e+00 0.000000e+00
## Q120014.fctrNo 0.000000e+00 0.000000e+00
## Q120014.fctrYes 0.000000e+00 0.000000e+00
## Q120379.fctrNo 0.000000e+00 0.000000e+00
## Q120379.fctrYes 0.000000e+00 0.000000e+00
## Q120472.fctrArt 0.000000e+00 0.000000e+00
## Q120472.fctrScience 0.000000e+00 0.000000e+00
## Q120650.fctrNo 0.000000e+00 0.000000e+00
## Q120650.fctrYes 0.000000e+00 0.000000e+00
## Q120978.fctrNo 0.000000e+00 0.000000e+00
## Q120978.fctrYes 0.000000e+00 0.000000e+00
## Q121011.fctrNo 0.000000e+00 0.000000e+00
## Q121011.fctrYes 0.000000e+00 0.000000e+00
## Q121700.fctrNo 0.000000e+00 0.000000e+00
## Q121700.fctrYes 0.000000e+00 0.000000e+00
## Q122120.fctrNo 0.000000e+00 0.000000e+00
## Q122120.fctrYes 0.000000e+00 0.000000e+00
## Q122769.fctrNo 0.000000e+00 0.000000e+00
## Q122769.fctrYes 0.000000e+00 0.000000e+00
## Q122770.fctrNo 0.000000e+00 0.000000e+00
## Q122770.fctrYes 0.000000e+00 0.000000e+00
## Q122771.fctrPc 0.000000e+00 0.000000e+00
## Q122771.fctrPt 0.000000e+00 0.000000e+00
## Q123464.fctrNo 0.000000e+00 0.000000e+00
## Q123464.fctrYes 0.000000e+00 0.000000e+00
## Q123621.fctrNo 0.000000e+00 0.000000e+00
## Q123621.fctrYes 0.000000e+00 0.000000e+00
## Q124122.fctrNo 0.000000e+00 0.000000e+00
## Q124122.fctrYes 0.000000e+00 0.000000e+00
## Q124742.fctrYes 0.000000e+00 0.000000e+00
## Q96024.fctrNo 0.000000e+00 0.000000e+00
## Q96024.fctrYes 0.000000e+00 0.000000e+00
## Q98059.fctrOnly-child 0.000000e+00 0.000000e+00
## Q98059.fctrYes 0.000000e+00 0.000000e+00
## Q98078.fctrNo 0.000000e+00 0.000000e+00
## Q98078.fctrYes 0.000000e+00 0.000000e+00
## Q98578.fctrNo 0.000000e+00 0.000000e+00
## Q98869.fctrNo 0.000000e+00 0.000000e+00
## Q98869.fctrYes 0.000000e+00 0.000000e+00
## Q99480.fctrNo 0.000000e+00 0.000000e+00
## Q99480.fctrYes 0.000000e+00 0.000000e+00
## Q99581.fctrNo 0.000000e+00 0.000000e+00
## Q99581.fctrYes 0.000000e+00 0.000000e+00
## Q99716.fctrNo 0.000000e+00 0.000000e+00
## Q99716.fctrYes 0.000000e+00 0.000000e+00
## Q99982.fctrCheck! 0.000000e+00 0.000000e+00
## Q99982.fctrNope 0.000000e+00 0.000000e+00
## YOB.Age.fctr.L 0.000000e+00 0.000000e+00
## YOB.Age.fctr.Q 0.000000e+00 0.000000e+00
## YOB.Age.fctr.C 0.000000e+00 0.000000e+00
## YOB.Age.fctr^4 0.000000e+00 0.000000e+00
## YOB.Age.fctr^5 0.000000e+00 0.000000e+00
## YOB.Age.fctr^6 0.000000e+00 0.000000e+00
## YOB.Age.fctr^8 0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr2 0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr3 0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr4 0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr5 0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr5 0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr5 0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr5 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr5 0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr5 0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr5 0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr6 0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr6 0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr6 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr6 0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr6 0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr6 0.000000e+00 0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.000000e+00 0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.000000e+00 0.000000e+00## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 3895 R 0.1177019
## 2 2446 R 0.1267051
## 3 2749 R 0.1558417
## 4 4762 R 0.1639864
## 5 5800 R 0.1704307
## 6 5782 R 0.1969376
## 7 1345 D 0.1348319
## 8 2882 D 0.1166666
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## 7 D FALSE
## 8 D FALSE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.8822981 FALSE
## 2 0.8732949 FALSE
## 3 0.8441583 FALSE
## 4 0.8360136 FALSE
## 5 0.8295693 FALSE
## 6 0.8030624 FALSE
## 7 0.1348319 TRUE
## 8 0.1166666 TRUE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.4822981
## 2 FALSE -0.4732949
## 3 FALSE -0.4441583
## 4 FALSE -0.4360136
## 5 FALSE -0.4295693
## 6 FALSE -0.4030624
## 7 TRUE 0.0000000
## 8 TRUE 0.0000000
## .label
## 1 3895
## 2 2446
## 3 2749
## 4 4762
## 5 5800
## 6 5782
## 7 1345
## 8 2882
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 3895 R 0.1177019
## 2 626 R 0.1235382
## 3 2446 R 0.1267051
## 4 3212 R 0.1332330
## 5 1883 R 0.1341627
## 6 4264 R 0.1512476
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 D TRUE
## 5 D TRUE
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.8822981 FALSE
## 2 0.8764618 FALSE
## 3 0.8732949 FALSE
## 4 0.8667670 FALSE
## 5 0.8658373 FALSE
## 6 0.8487524 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.4822981
## 2 FALSE -0.4764618
## 3 FALSE -0.4732949
## 4 FALSE -0.4667670
## 5 FALSE -0.4658373
## 6 FALSE -0.4487524
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 10 1352 R 0.1605493
## 26 3262 R 0.2099293
## 28 6045 R 0.2115404
## 32 2252 R 0.2300111
## 37 6135 R 0.2511529
## 41 445 R 0.2719164
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 10 D TRUE
## 26 D TRUE
## 28 D TRUE
## 32 D TRUE
## 37 D TRUE
## 41 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 10 0.8394507
## 26 0.7900707
## 28 0.7884596
## 32 0.7699889
## 37 0.7488471
## 41 0.7280836
## Party.fctr.All.X..rcv.glmnet.is.acc
## 10 FALSE
## 26 FALSE
## 28 FALSE
## 32 FALSE
## 37 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 10 FALSE
## 26 FALSE
## 28 FALSE
## 32 FALSE
## 37 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 10 -0.4394507
## 26 -0.3900707
## 28 -0.3884596
## 32 -0.3699889
## 37 -0.3488471
## 41 -0.3280836
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 36 4010 R 0.2478407
## 37 6135 R 0.2511529
## 38 3640 R 0.2565594
## 39 5466 R 0.2611470
## 40 2799 R 0.2659742
## 41 445 R 0.2719164
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 36 D TRUE
## 37 D TRUE
## 38 D TRUE
## 39 D TRUE
## 40 D TRUE
## 41 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 36 0.7521593
## 37 0.7488471
## 38 0.7434406
## 39 0.7388530
## 40 0.7340258
## 41 0.7280836
## Party.fctr.All.X..rcv.glmnet.is.acc
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 36 FALSE
## 37 FALSE
## 38 FALSE
## 39 FALSE
## 40 FALSE
## 41 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 36 -0.3521593
## 37 -0.3488471
## 38 -0.3434406
## 39 -0.3388530
## 40 -0.3340258
## 41 -0.3280836
## Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy PKy 3 8 2 0.01092896 0.01554404
## N N 9 40 10 0.05464481 0.04663212
## MKy MKy 47 182 55 0.24863388 0.24352332
## SKy SKy 9 37 10 0.05054645 0.04663212
## MKn MKn 23 100 26 0.13661202 0.11917098
## SKn SKn 93 324 110 0.44262295 0.48186528
## PKn PKn 9 41 10 0.05601093 0.04663212
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy 0.00896861 1.415925 0.1769906 8 1.243398
## N 0.04484305 10.618246 0.2654561 40 3.510021
## MKy 0.24663677 52.182641 0.2867178 182 15.160971
## SKy 0.04484305 9.444703 0.2552622 37 2.878612
## MKn 0.11659193 31.035907 0.3103591 100 7.342244
## SKn 0.49327354 105.501365 0.3256215 324 29.134241
## PKn 0.04484305 8.686566 0.2118675 41 2.779787
## err.abs.OOB.mean
## PKy 0.4144660
## N 0.3900023
## MKy 0.3225738
## SKy 0.3198458
## MKn 0.3192280
## SKn 0.3132714
## PKn 0.3088652
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 193.000000 732.000000 223.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 218.885353 1.832275
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 732.000000 62.049273 2.388253## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 178.921 NA NA## label step_major step_minor label_minor bgn end elapsed
## 6 fit.models 4 2 2 171.471 178.93 7.459
## 7 fit.models 4 3 3 178.930 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 7 fit.models 4 3 3 178.930 181.846
## 8 fit.data.training 5 0 0 181.847 NA
## elapsed
## 7 2.916
## 8 NAStep 5.0: fit data training
```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X###glmnet"
## [1] " indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.737000 secs"
## Fitting alpha = 0.55, lambda = 0.04 on full training set
## [1] "myfit_mdl: train complete: 3.250000 secs"
## alpha lambda
## 1 0.55 0.04
## Length Class Mode
## a0 86 -none- numeric
## beta 23564 dgCMatrix S4
## df 86 -none- numeric
## dim 2 -none- numeric
## lambda 86 -none- numeric
## dev.ratio 86 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 274 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.247949359 -0.135662592
## Edn.fctr^5 Q100680.fctrNo
## 0.137102512 0.044543376
## Q100689.fctrNo Q110740.fctrMac
## 0.018798140 -0.037253725
## Q112478.fctrNo Q114152.fctrYes
## 0.105380230 -0.073889597
## Q114517.fctrYes Q115611.fctrYes
## -0.005237977 0.001657426
## Q116197.fctrA.M. Q116953.fctrNo
## 0.047669095 0.009812758
## Q118232.fctrId Q119851.fctrNo
## -0.251720827 0.010679633
## Q120194.fctrStudy first Q120194.fctrTry first
## -0.142922459 0.017473595
## Q121699.fctrNo Q124122.fctrYes
## 0.077677438 -0.012522799
## Q124742.fctrNo Q98197.fctrNo
## -0.040963097 -0.108040223
## YOB.Age.fctr^7 Hhold.fctrMKn:.clusterid.fctr2
## 0.044813073 0.047062161
## Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff
## 0.861171495 0.007886025
## [1] "max lambda < lambdaOpt:"
## (Intercept) Edn.fctr.Q
## -1.240309458 -0.181216995
## Edn.fctr.C Edn.fctr^5
## 0.021575515 0.158619647
## Q100680.fctrNo Q100689.fctrNo
## 0.065498098 0.032602770
## Q110740.fctrMac Q112270.fctrNo
## -0.057209954 -0.003871040
## Q112478.fctrNo Q114152.fctrYes
## 0.129215957 -0.103656048
## Q114517.fctrYes Q115611.fctrYes
## -0.037406135 0.030322176
## Q116197.fctrA.M. Q116197.fctrP.M.
## 0.076472844 -0.003296449
## Q116953.fctrNo Q117186.fctrCool headed
## 0.034222174 0.023049342
## Q118232.fctrId Q119851.fctrNo
## -0.288995464 0.040128102
## Q120194.fctrStudy first Q120194.fctrTry first
## -0.163057233 0.033305627
## Q121699.fctrNo Q124122.fctrYes
## 0.088978128 -0.028607823
## Q124742.fctrNo Q98197.fctrNo
## -0.069527698 -0.142051798
## YOB.Age.fctr^7 Hhold.fctrMKn:.clusterid.fctr2
## 0.086250907 0.113875995
## Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff
## 0.971013197 0.011553732
## [1] "myfit_mdl: train diagnostics complete: 3.323000 secs"
## Prediction
## Reference D R
## D 722 20
## R 158 25
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.075676e-01 1.531657e-01 7.806592e-01 8.325006e-01 8.021622e-01
## AccuracyPValue McnemarPValue
## 3.578570e-01 9.762690e-25
## [1] "myfit_mdl: predict complete: 8.136000 secs"
## id
## 1 Final.All.X###glmnet
## feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 2.429 1.583
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5 1 0 0.6988497
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.25 0.2192982 0.8075676
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7806592 0.8325006 0.1531657
## [1] "myfit_mdl: exit: 8.158000 secs"## label step_major step_minor label_minor bgn end
## 8 fit.data.training 5 0 0 181.847 190.473
## 9 fit.data.training 5 1 1 190.474 NA
## elapsed
## 8 8.627
## 9 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glb_fin_mdl uses the same coefficients as glb_sel_mdl,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.6glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp
## Hhold.fctrN:.clusterid.fctr6 1.000000e+02
## Q118232.fctrId 1.285848e+01
## Edn.fctr.Q 1.745373e+01
## Q120194.fctrStudy first 6.689618e+00
## Edn.fctr^5 1.201560e+01
## Q98197.fctrNo 1.440277e+01
## Q112478.fctrNo 6.116418e+00
## Q114152.fctrYes 7.764113e+00
## Q121699.fctrNo 4.435750e+00
## Hhold.fctrMKn:.clusterid.fctr2 2.261710e-01
## YOB.Age.fctr^7 5.141455e-01
## Q116197.fctrA.M. 4.410901e+00
## Q100680.fctrNo 4.005389e-01
## Q124742.fctrNo 1.835248e-01
## Q110740.fctrMac 0.000000e+00
## Q100689.fctrNo 0.000000e+00
## Q120194.fctrTry first 7.718628e+00
## Q119851.fctrNo 7.891620e-01
## Q124122.fctrYes 0.000000e+00
## Q116953.fctrNo 4.285022e+00
## Q114517.fctrYes 1.354323e-01
## Q115611.fctrYes 0.000000e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff 2.357253e+00
## Q117186.fctrCool headed 9.590309e-02
## Edn.fctr.C 0.000000e+00
## Q112270.fctrNo 0.000000e+00
## Q116197.fctrP.M. 0.000000e+00
## .rnorm 0.000000e+00
## Edn.fctr.L 0.000000e+00
## Edn.fctr^4 0.000000e+00
## Edn.fctr^6 0.000000e+00
## Edn.fctr^7 0.000000e+00
## Gender.fctrF 0.000000e+00
## Gender.fctrM 0.000000e+00
## Hhold.fctrMKn 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr3 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr4 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr5 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr6 0.000000e+00
## Hhold.fctrMKy 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr2 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr3 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr4 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr5 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr6 0.000000e+00
## Hhold.fctrN:.clusterid.fctr2 0.000000e+00
## Hhold.fctrN:.clusterid.fctr3 0.000000e+00
## Hhold.fctrN:.clusterid.fctr4 0.000000e+00
## Hhold.fctrN:.clusterid.fctr5 0.000000e+00
## Hhold.fctrPKn 3.310930e+00
## Hhold.fctrPKn:.clusterid.fctr2 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr3 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr4 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr5 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr6 0.000000e+00
## Hhold.fctrPKy 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr2 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr3 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr4 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr5 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr6 0.000000e+00
## Hhold.fctrSKn 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr2 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr3 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr4 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr5 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr6 0.000000e+00
## Hhold.fctrSKy 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr2 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr3 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr4 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr5 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr6 0.000000e+00
## Income.fctr.C 0.000000e+00
## Income.fctr.L 0.000000e+00
## Income.fctr.Q 0.000000e+00
## Income.fctr^4 0.000000e+00
## Income.fctr^5 0.000000e+00
## Income.fctr^6 0.000000e+00
## Q100010.fctrNo 0.000000e+00
## Q100010.fctrYes 0.000000e+00
## Q100562.fctrNo 5.132660e+00
## Q100562.fctrYes 0.000000e+00
## Q100680.fctrYes 0.000000e+00
## Q100689.fctrYes 0.000000e+00
## Q101162.fctrOptimist 0.000000e+00
## Q101162.fctrPessimist 0.000000e+00
## Q101163.fctrDad 0.000000e+00
## Q101163.fctrMom 0.000000e+00
## Q101596.fctrNo 4.912269e-01
## Q101596.fctrYes 0.000000e+00
## Q102089.fctrOwn 0.000000e+00
## Q102089.fctrRent 0.000000e+00
## Q102289.fctrNo 0.000000e+00
## Q102289.fctrYes 0.000000e+00
## Q102674.fctrNo 0.000000e+00
## Q102674.fctrYes 0.000000e+00
## Q102687.fctrNo 0.000000e+00
## Q102687.fctrYes 0.000000e+00
## Q102906.fctrNo 0.000000e+00
## Q102906.fctrYes 0.000000e+00
## Q103293.fctrNo 0.000000e+00
## Q103293.fctrYes 0.000000e+00
## Q104996.fctrNo 0.000000e+00
## Q104996.fctrYes 0.000000e+00
## Q105655.fctrNo 0.000000e+00
## Q105655.fctrYes 0.000000e+00
## Q105840.fctrNo 0.000000e+00
## Q105840.fctrYes 0.000000e+00
## Q106042.fctrNo 0.000000e+00
## Q106042.fctrYes 0.000000e+00
## Q106272.fctrNo 4.578403e-02
## Q106272.fctrYes 0.000000e+00
## Q106388.fctrNo 0.000000e+00
## Q106388.fctrYes 0.000000e+00
## Q106389.fctrNo 0.000000e+00
## Q106389.fctrYes 0.000000e+00
## Q106993.fctrNo 0.000000e+00
## Q106993.fctrYes 0.000000e+00
## Q106997.fctrGr 0.000000e+00
## Q106997.fctrYy 0.000000e+00
## Q107491.fctrNo 0.000000e+00
## Q107491.fctrYes 0.000000e+00
## Q107869.fctrNo 0.000000e+00
## Q107869.fctrYes 0.000000e+00
## Q108342.fctrIn-person 0.000000e+00
## Q108342.fctrOnline 0.000000e+00
## Q108343.fctrNo 0.000000e+00
## Q108343.fctrYes 0.000000e+00
## Q108617.fctrNo 0.000000e+00
## Q108617.fctrYes 0.000000e+00
## Q108754.fctrNo 0.000000e+00
## Q108754.fctrYes 0.000000e+00
## Q108855.fctrUmm... 0.000000e+00
## Q108855.fctrYes! 0.000000e+00
## Q108856.fctrSocialize 0.000000e+00
## Q108856.fctrSpace 0.000000e+00
## Q108950.fctrCautious 0.000000e+00
## Q108950.fctrRisk-friendly 0.000000e+00
## Q109367.fctrNo 0.000000e+00
## Q109367.fctrYes 0.000000e+00
## Q110740.fctrPC 0.000000e+00
## Q111220.fctrNo 0.000000e+00
## Q111220.fctrYes 0.000000e+00
## Q111580.fctrDemanding 0.000000e+00
## Q111580.fctrSupportive 0.000000e+00
## Q111848.fctrNo 0.000000e+00
## Q111848.fctrYes 0.000000e+00
## Q112270.fctrYes 0.000000e+00
## Q112478.fctrYes 0.000000e+00
## Q112512.fctrNo 0.000000e+00
## Q112512.fctrYes 0.000000e+00
## Q113181.fctrNo 0.000000e+00
## Q113181.fctrYes 0.000000e+00
## Q113583.fctrTalk 0.000000e+00
## Q113583.fctrTunes 0.000000e+00
## Q113584.fctrPeople 0.000000e+00
## Q113584.fctrTechnology 0.000000e+00
## Q113992.fctrNo 0.000000e+00
## Q113992.fctrYes 0.000000e+00
## Q114152.fctrNo 0.000000e+00
## Q114386.fctrMysterious 0.000000e+00
## Q114386.fctrTMI 0.000000e+00
## Q114517.fctrNo 4.911950e-02
## Q114748.fctrNo 0.000000e+00
## Q114748.fctrYes 0.000000e+00
## Q114961.fctrNo 0.000000e+00
## Q114961.fctrYes 0.000000e+00
## Q115195.fctrNo 0.000000e+00
## Q115195.fctrYes 0.000000e+00
## Q115390.fctrNo 2.988845e-01
## Q115390.fctrYes 0.000000e+00
## Q115602.fctrNo 0.000000e+00
## Q115602.fctrYes 0.000000e+00
## Q115610.fctrNo 7.604491e+00
## Q115610.fctrYes 0.000000e+00
## Q115611.fctrNo 0.000000e+00
## Q115777.fctrEnd 0.000000e+00
## Q115777.fctrStart 0.000000e+00
## Q115899.fctrCs 0.000000e+00
## Q115899.fctrMe 0.000000e+00
## Q116441.fctrNo 0.000000e+00
## Q116441.fctrYes 0.000000e+00
## Q116448.fctrNo 0.000000e+00
## Q116448.fctrYes 0.000000e+00
## Q116601.fctrNo 0.000000e+00
## Q116601.fctrYes 0.000000e+00
## Q116797.fctrNo 0.000000e+00
## Q116797.fctrYes 0.000000e+00
## Q116881.fctrHappy 0.000000e+00
## Q116881.fctrRight 0.000000e+00
## Q116953.fctrYes 0.000000e+00
## Q117186.fctrHot headed 0.000000e+00
## Q117193.fctrOdd hours 0.000000e+00
## Q117193.fctrStandard hours 7.941692e-01
## Q118117.fctrNo 0.000000e+00
## Q118117.fctrYes 0.000000e+00
## Q118232.fctrPr 0.000000e+00
## Q118233.fctrNo 0.000000e+00
## Q118233.fctrYes 0.000000e+00
## Q118237.fctrNo 0.000000e+00
## Q118237.fctrYes 0.000000e+00
## Q118892.fctrNo 0.000000e+00
## Q118892.fctrYes 0.000000e+00
## Q119334.fctrNo 0.000000e+00
## Q119334.fctrYes 0.000000e+00
## Q119650.fctrGiving 0.000000e+00
## Q119650.fctrReceiving 0.000000e+00
## Q119851.fctrYes 0.000000e+00
## Q120012.fctrNo 0.000000e+00
## Q120012.fctrYes 0.000000e+00
## Q120014.fctrNo 0.000000e+00
## Q120014.fctrYes 0.000000e+00
## Q120379.fctrNo 0.000000e+00
## Q120379.fctrYes 0.000000e+00
## Q120472.fctrArt 0.000000e+00
## Q120472.fctrScience 0.000000e+00
## Q120650.fctrNo 0.000000e+00
## Q120650.fctrYes 0.000000e+00
## Q120978.fctrNo 0.000000e+00
## Q120978.fctrYes 0.000000e+00
## Q121011.fctrNo 0.000000e+00
## Q121011.fctrYes 0.000000e+00
## Q121699.fctrYes 3.658572e+00
## Q121700.fctrNo 0.000000e+00
## Q121700.fctrYes 0.000000e+00
## Q122120.fctrNo 0.000000e+00
## Q122120.fctrYes 0.000000e+00
## Q122769.fctrNo 0.000000e+00
## Q122769.fctrYes 0.000000e+00
## Q122770.fctrNo 0.000000e+00
## Q122770.fctrYes 0.000000e+00
## Q122771.fctrPc 0.000000e+00
## Q122771.fctrPt 0.000000e+00
## Q123464.fctrNo 0.000000e+00
## Q123464.fctrYes 0.000000e+00
## Q123621.fctrNo 0.000000e+00
## Q123621.fctrYes 0.000000e+00
## Q124122.fctrNo 0.000000e+00
## Q124742.fctrYes 0.000000e+00
## Q96024.fctrNo 0.000000e+00
## Q96024.fctrYes 0.000000e+00
## Q98059.fctrOnly-child 0.000000e+00
## Q98059.fctrYes 0.000000e+00
## Q98078.fctrNo 0.000000e+00
## Q98078.fctrYes 0.000000e+00
## Q98197.fctrYes 7.308264e-03
## Q98578.fctrNo 0.000000e+00
## Q98578.fctrYes 3.896913e+00
## Q98869.fctrNo 0.000000e+00
## Q98869.fctrYes 0.000000e+00
## Q99480.fctrNo 0.000000e+00
## Q99480.fctrYes 0.000000e+00
## Q99581.fctrNo 0.000000e+00
## Q99581.fctrYes 0.000000e+00
## Q99716.fctrNo 0.000000e+00
## Q99716.fctrYes 0.000000e+00
## Q99982.fctrCheck! 0.000000e+00
## Q99982.fctrNope 0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr.C 0.000000e+00
## YOB.Age.fctr.L 0.000000e+00
## YOB.Age.fctr.Q 0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff 0.000000e+00
## YOB.Age.fctr^4 0.000000e+00
## YOB.Age.fctr^5 0.000000e+00
## YOB.Age.fctr^6 0.000000e+00
## YOB.Age.fctr^8 0.000000e+00
## Final.All.X...glmnet.imp imp
## Hhold.fctrN:.clusterid.fctr6 100.0000000 100.0000000
## Q118232.fctrId 29.4152402 29.4152402
## Edn.fctr.Q 16.7656471 16.7656471
## Q120194.fctrStudy first 16.6645549 16.6645549
## Edn.fctr^5 16.0648774 16.0648774
## Q98197.fctrNo 13.2707242 13.2707242
## Q112478.fctrNo 12.6093429 12.6093429
## Q114152.fctrYes 9.3090924 9.3090924
## Q121699.fctrNo 9.0698923 9.0698923
## Hhold.fctrMKn:.clusterid.fctr2 7.6441098 7.6441098
## YOB.Age.fctr^7 6.4838565 6.4838565
## Q116197.fctrA.M. 6.3496943 6.3496943
## Q100680.fctrNo 5.7197445 5.7197445
## Q124742.fctrNo 5.5930697 5.5930697
## Q110740.fctrMac 4.8708017 4.8708017
## Q100689.fctrNo 2.5916328 2.5916328
## Q120194.fctrTry first 2.5165332 2.5165332
## Q119851.fctrNo 2.2466212 2.2466212
## Q124122.fctrYes 1.9733377 1.9733377
## Q116953.fctrNo 1.9693431 1.9693431
## Q114517.fctrYes 1.7370660 1.7370660
## Q115611.fctrYes 1.2121081 1.2121081
## YOB.Age.fctr(15,20]:YOB.Age.dff 1.0111216 1.0111216
## Q117186.fctrCool headed 0.8259899 0.8259899
## Edn.fctr.C 0.7731743 0.7731743
## Q112270.fctrNo 0.1387215 0.1387215
## Q116197.fctrP.M. 0.1181306 0.1181306
## .rnorm 0.0000000 0.0000000
## Edn.fctr.L 0.0000000 0.0000000
## Edn.fctr^4 0.0000000 0.0000000
## Edn.fctr^6 0.0000000 0.0000000
## Edn.fctr^7 0.0000000 0.0000000
## Gender.fctrF 0.0000000 0.0000000
## Gender.fctrM 0.0000000 0.0000000
## Hhold.fctrMKn 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr6 0.0000000 0.0000000
## Hhold.fctrMKy 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr6 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrPKn 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr6 0.0000000 0.0000000
## Hhold.fctrPKy 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr6 0.0000000 0.0000000
## Hhold.fctrSKn 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr6 0.0000000 0.0000000
## Hhold.fctrSKy 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr5 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr6 0.0000000 0.0000000
## Income.fctr.C 0.0000000 0.0000000
## Income.fctr.L 0.0000000 0.0000000
## Income.fctr.Q 0.0000000 0.0000000
## Income.fctr^4 0.0000000 0.0000000
## Income.fctr^5 0.0000000 0.0000000
## Income.fctr^6 0.0000000 0.0000000
## Q100010.fctrNo 0.0000000 0.0000000
## Q100010.fctrYes 0.0000000 0.0000000
## Q100562.fctrNo 0.0000000 0.0000000
## Q100562.fctrYes 0.0000000 0.0000000
## Q100680.fctrYes 0.0000000 0.0000000
## Q100689.fctrYes 0.0000000 0.0000000
## Q101162.fctrOptimist 0.0000000 0.0000000
## Q101162.fctrPessimist 0.0000000 0.0000000
## Q101163.fctrDad 0.0000000 0.0000000
## Q101163.fctrMom 0.0000000 0.0000000
## Q101596.fctrNo 0.0000000 0.0000000
## Q101596.fctrYes 0.0000000 0.0000000
## Q102089.fctrOwn 0.0000000 0.0000000
## Q102089.fctrRent 0.0000000 0.0000000
## Q102289.fctrNo 0.0000000 0.0000000
## Q102289.fctrYes 0.0000000 0.0000000
## Q102674.fctrNo 0.0000000 0.0000000
## Q102674.fctrYes 0.0000000 0.0000000
## Q102687.fctrNo 0.0000000 0.0000000
## Q102687.fctrYes 0.0000000 0.0000000
## Q102906.fctrNo 0.0000000 0.0000000
## Q102906.fctrYes 0.0000000 0.0000000
## Q103293.fctrNo 0.0000000 0.0000000
## Q103293.fctrYes 0.0000000 0.0000000
## Q104996.fctrNo 0.0000000 0.0000000
## Q104996.fctrYes 0.0000000 0.0000000
## Q105655.fctrNo 0.0000000 0.0000000
## Q105655.fctrYes 0.0000000 0.0000000
## Q105840.fctrNo 0.0000000 0.0000000
## Q105840.fctrYes 0.0000000 0.0000000
## Q106042.fctrNo 0.0000000 0.0000000
## Q106042.fctrYes 0.0000000 0.0000000
## Q106272.fctrNo 0.0000000 0.0000000
## Q106272.fctrYes 0.0000000 0.0000000
## Q106388.fctrNo 0.0000000 0.0000000
## Q106388.fctrYes 0.0000000 0.0000000
## Q106389.fctrNo 0.0000000 0.0000000
## Q106389.fctrYes 0.0000000 0.0000000
## Q106993.fctrNo 0.0000000 0.0000000
## Q106993.fctrYes 0.0000000 0.0000000
## Q106997.fctrGr 0.0000000 0.0000000
## Q106997.fctrYy 0.0000000 0.0000000
## Q107491.fctrNo 0.0000000 0.0000000
## Q107491.fctrYes 0.0000000 0.0000000
## Q107869.fctrNo 0.0000000 0.0000000
## Q107869.fctrYes 0.0000000 0.0000000
## Q108342.fctrIn-person 0.0000000 0.0000000
## Q108342.fctrOnline 0.0000000 0.0000000
## Q108343.fctrNo 0.0000000 0.0000000
## Q108343.fctrYes 0.0000000 0.0000000
## Q108617.fctrNo 0.0000000 0.0000000
## Q108617.fctrYes 0.0000000 0.0000000
## Q108754.fctrNo 0.0000000 0.0000000
## Q108754.fctrYes 0.0000000 0.0000000
## Q108855.fctrUmm... 0.0000000 0.0000000
## Q108855.fctrYes! 0.0000000 0.0000000
## Q108856.fctrSocialize 0.0000000 0.0000000
## Q108856.fctrSpace 0.0000000 0.0000000
## Q108950.fctrCautious 0.0000000 0.0000000
## Q108950.fctrRisk-friendly 0.0000000 0.0000000
## Q109367.fctrNo 0.0000000 0.0000000
## Q109367.fctrYes 0.0000000 0.0000000
## Q110740.fctrPC 0.0000000 0.0000000
## Q111220.fctrNo 0.0000000 0.0000000
## Q111220.fctrYes 0.0000000 0.0000000
## Q111580.fctrDemanding 0.0000000 0.0000000
## Q111580.fctrSupportive 0.0000000 0.0000000
## Q111848.fctrNo 0.0000000 0.0000000
## Q111848.fctrYes 0.0000000 0.0000000
## Q112270.fctrYes 0.0000000 0.0000000
## Q112478.fctrYes 0.0000000 0.0000000
## Q112512.fctrNo 0.0000000 0.0000000
## Q112512.fctrYes 0.0000000 0.0000000
## Q113181.fctrNo 0.0000000 0.0000000
## Q113181.fctrYes 0.0000000 0.0000000
## Q113583.fctrTalk 0.0000000 0.0000000
## Q113583.fctrTunes 0.0000000 0.0000000
## Q113584.fctrPeople 0.0000000 0.0000000
## Q113584.fctrTechnology 0.0000000 0.0000000
## Q113992.fctrNo 0.0000000 0.0000000
## Q113992.fctrYes 0.0000000 0.0000000
## Q114152.fctrNo 0.0000000 0.0000000
## Q114386.fctrMysterious 0.0000000 0.0000000
## Q114386.fctrTMI 0.0000000 0.0000000
## Q114517.fctrNo 0.0000000 0.0000000
## Q114748.fctrNo 0.0000000 0.0000000
## Q114748.fctrYes 0.0000000 0.0000000
## Q114961.fctrNo 0.0000000 0.0000000
## Q114961.fctrYes 0.0000000 0.0000000
## Q115195.fctrNo 0.0000000 0.0000000
## Q115195.fctrYes 0.0000000 0.0000000
## Q115390.fctrNo 0.0000000 0.0000000
## Q115390.fctrYes 0.0000000 0.0000000
## Q115602.fctrNo 0.0000000 0.0000000
## Q115602.fctrYes 0.0000000 0.0000000
## Q115610.fctrNo 0.0000000 0.0000000
## Q115610.fctrYes 0.0000000 0.0000000
## Q115611.fctrNo 0.0000000 0.0000000
## Q115777.fctrEnd 0.0000000 0.0000000
## Q115777.fctrStart 0.0000000 0.0000000
## Q115899.fctrCs 0.0000000 0.0000000
## Q115899.fctrMe 0.0000000 0.0000000
## Q116441.fctrNo 0.0000000 0.0000000
## Q116441.fctrYes 0.0000000 0.0000000
## Q116448.fctrNo 0.0000000 0.0000000
## Q116448.fctrYes 0.0000000 0.0000000
## Q116601.fctrNo 0.0000000 0.0000000
## Q116601.fctrYes 0.0000000 0.0000000
## Q116797.fctrNo 0.0000000 0.0000000
## Q116797.fctrYes 0.0000000 0.0000000
## Q116881.fctrHappy 0.0000000 0.0000000
## Q116881.fctrRight 0.0000000 0.0000000
## Q116953.fctrYes 0.0000000 0.0000000
## Q117186.fctrHot headed 0.0000000 0.0000000
## Q117193.fctrOdd hours 0.0000000 0.0000000
## Q117193.fctrStandard hours 0.0000000 0.0000000
## Q118117.fctrNo 0.0000000 0.0000000
## Q118117.fctrYes 0.0000000 0.0000000
## Q118232.fctrPr 0.0000000 0.0000000
## Q118233.fctrNo 0.0000000 0.0000000
## Q118233.fctrYes 0.0000000 0.0000000
## Q118237.fctrNo 0.0000000 0.0000000
## Q118237.fctrYes 0.0000000 0.0000000
## Q118892.fctrNo 0.0000000 0.0000000
## Q118892.fctrYes 0.0000000 0.0000000
## Q119334.fctrNo 0.0000000 0.0000000
## Q119334.fctrYes 0.0000000 0.0000000
## Q119650.fctrGiving 0.0000000 0.0000000
## Q119650.fctrReceiving 0.0000000 0.0000000
## Q119851.fctrYes 0.0000000 0.0000000
## Q120012.fctrNo 0.0000000 0.0000000
## Q120012.fctrYes 0.0000000 0.0000000
## Q120014.fctrNo 0.0000000 0.0000000
## Q120014.fctrYes 0.0000000 0.0000000
## Q120379.fctrNo 0.0000000 0.0000000
## Q120379.fctrYes 0.0000000 0.0000000
## Q120472.fctrArt 0.0000000 0.0000000
## Q120472.fctrScience 0.0000000 0.0000000
## Q120650.fctrNo 0.0000000 0.0000000
## Q120650.fctrYes 0.0000000 0.0000000
## Q120978.fctrNo 0.0000000 0.0000000
## Q120978.fctrYes 0.0000000 0.0000000
## Q121011.fctrNo 0.0000000 0.0000000
## Q121011.fctrYes 0.0000000 0.0000000
## Q121699.fctrYes 0.0000000 0.0000000
## Q121700.fctrNo 0.0000000 0.0000000
## Q121700.fctrYes 0.0000000 0.0000000
## Q122120.fctrNo 0.0000000 0.0000000
## Q122120.fctrYes 0.0000000 0.0000000
## Q122769.fctrNo 0.0000000 0.0000000
## Q122769.fctrYes 0.0000000 0.0000000
## Q122770.fctrNo 0.0000000 0.0000000
## Q122770.fctrYes 0.0000000 0.0000000
## Q122771.fctrPc 0.0000000 0.0000000
## Q122771.fctrPt 0.0000000 0.0000000
## Q123464.fctrNo 0.0000000 0.0000000
## Q123464.fctrYes 0.0000000 0.0000000
## Q123621.fctrNo 0.0000000 0.0000000
## Q123621.fctrYes 0.0000000 0.0000000
## Q124122.fctrNo 0.0000000 0.0000000
## Q124742.fctrYes 0.0000000 0.0000000
## Q96024.fctrNo 0.0000000 0.0000000
## Q96024.fctrYes 0.0000000 0.0000000
## Q98059.fctrOnly-child 0.0000000 0.0000000
## Q98059.fctrYes 0.0000000 0.0000000
## Q98078.fctrNo 0.0000000 0.0000000
## Q98078.fctrYes 0.0000000 0.0000000
## Q98197.fctrYes 0.0000000 0.0000000
## Q98578.fctrNo 0.0000000 0.0000000
## Q98578.fctrYes 0.0000000 0.0000000
## Q98869.fctrNo 0.0000000 0.0000000
## Q98869.fctrYes 0.0000000 0.0000000
## Q99480.fctrNo 0.0000000 0.0000000
## Q99480.fctrYes 0.0000000 0.0000000
## Q99581.fctrNo 0.0000000 0.0000000
## Q99581.fctrYes 0.0000000 0.0000000
## Q99716.fctrNo 0.0000000 0.0000000
## Q99716.fctrYes 0.0000000 0.0000000
## Q99982.fctrCheck! 0.0000000 0.0000000
## Q99982.fctrNope 0.0000000 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr.C 0.0000000 0.0000000
## YOB.Age.fctr.L 0.0000000 0.0000000
## YOB.Age.fctr.Q 0.0000000 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000 0.0000000
## YOB.Age.fctr^4 0.0000000 0.0000000
## YOB.Age.fctr^5 0.0000000 0.0000000
## YOB.Age.fctr^6 0.0000000 0.0000000
## YOB.Age.fctr^8 0.0000000 0.0000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 5638 R 0.1432618
## 2 4506 R 0.1402091
## 3 468 R 0.1568337
## 4 3212 R NA
## 5 626 R NA
## 6 4785 R 0.1709531
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 D TRUE
## 2 D TRUE
## 3 D TRUE
## 4 <NA> NA
## 5 <NA> NA
## 6 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.8567382 FALSE
## 2 0.8597909 FALSE
## 3 0.8431663 FALSE
## 4 NA NA
## 5 NA NA
## 6 0.8290469 FALSE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 1 0.1501717 D
## 2 0.1508326 D
## 3 0.1541281 D
## 4 0.1546975 D
## 5 0.1561392 D
## 6 0.1593257 D
## Party.fctr.Final.All.X...glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 1 0.8498283
## 2 0.8491674
## 3 0.8458719
## 4 0.8453025
## 5 0.8438608
## 6 0.8406743
## Party.fctr.Final.All.X...glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 1 -0.4498283
## 2 -0.4491674
## 3 -0.4458719
## 4 -0.4453025
## 5 -0.4438608
## 6 -0.4406743
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 36 2638 R 0.1690641
## 75 1487 R 0.2121848
## 93 1569 R 0.2816323
## 98 2428 R 0.2111990
## 141 6197 R 0.2428644
## 162 4364 R 0.2927004
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 36 D TRUE
## 75 D TRUE
## 93 D TRUE
## 98 D TRUE
## 141 D TRUE
## 162 D TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 36 0.8309359
## 75 0.7878152
## 93 0.7183677
## 98 0.7888010
## 141 0.7571356
## 162 0.7072996
## Party.fctr.All.X..rcv.glmnet.is.acc
## 36 FALSE
## 75 FALSE
## 93 FALSE
## 98 FALSE
## 141 FALSE
## 162 FALSE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 36 0.1882236 D
## 75 0.2058497 D
## 93 0.2166311 D
## 98 0.2188063 D
## 141 0.2384300 D
## 162 0.2549770 D
## Party.fctr.Final.All.X...glmnet.err
## 36 TRUE
## 75 TRUE
## 93 TRUE
## 98 TRUE
## 141 TRUE
## 162 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 36 0.8117764
## 75 0.7941503
## 93 0.7833689
## 98 0.7811937
## 141 0.7615700
## 162 0.7450230
## Party.fctr.Final.All.X...glmnet.is.acc
## 36 FALSE
## 75 FALSE
## 93 FALSE
## 98 FALSE
## 141 FALSE
## 162 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 36 FALSE
## 75 FALSE
## 93 FALSE
## 98 FALSE
## 141 FALSE
## 162 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 36 -0.4117764
## 75 -0.3941503
## 93 -0.3833689
## 98 -0.3811937
## 141 -0.3615700
## 162 -0.3450230
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 178 749 R 0.3048864
## 179 3291 R 0.2948194
## 180 5291 R 0.2758853
## 181 1482 R 0.5202509
## 182 4552 R 0.6715854
## 183 5144 R 0.6827682
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 178 D TRUE
## 179 D TRUE
## 180 D TRUE
## 181 D TRUE
## 182 R FALSE
## 183 R FALSE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 178 0.6951136
## 179 0.7051806
## 180 0.7241147
## 181 0.4797491
## 182 0.3284146
## 183 0.3172318
## Party.fctr.All.X..rcv.glmnet.is.acc
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 TRUE
## 183 TRUE
## Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 178 0.2740269 D
## 179 0.2749663 D
## 180 0.2847975 D
## 181 0.3618322 D
## 182 0.4416022 D
## 183 0.4534200 D
## Party.fctr.Final.All.X...glmnet.err
## 178 TRUE
## 179 TRUE
## 180 TRUE
## 181 TRUE
## 182 TRUE
## 183 TRUE
## Party.fctr.Final.All.X...glmnet.err.abs
## 178 0.7259731
## 179 0.7250337
## 180 0.7152025
## 181 0.6381678
## 182 0.5583978
## 183 0.5465800
## Party.fctr.Final.All.X...glmnet.is.acc
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## Party.fctr.Final.All.X...glmnet.accurate
## 178 FALSE
## 179 FALSE
## 180 FALSE
## 181 FALSE
## 182 FALSE
## 183 FALSE
## Party.fctr.Final.All.X...glmnet.error
## 178 -0.3259731
## 179 -0.3250337
## 180 -0.3152025
## 181 -0.2381678
## 182 -0.1583978
## 183 -0.1465800
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final.All.X...glmnet.prob"
## [2] "Party.fctr.Final.All.X...glmnet"
## [3] "Party.fctr.Final.All.X...glmnet.err"
## [4] "Party.fctr.Final.All.X...glmnet.err.abs"
## [5] "Party.fctr.Final.All.X...glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 9 fit.data.training 5 1 1 190.474 196.152
## 10 predict.data.new 6 0 0 196.152 NA
## elapsed
## 9 5.678
## 10 NAStep 6.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.6
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.6## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## Warning: Removed 223 rows containing missing values (geom_point).
## NULL## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] "Stacking file Q109244NA_Ensemble_cnk03_rest_out_fin.csv to prediction outputs..."## [1] 0.6## [1] "glbMdlSelId: All.X##rcv#glmnet"## [1] "glbMdlFinId: Final.All.X###glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y##rcv#rpart
## 0 1
## Final.All.X###glmnet
## 0## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet 0.7875648 0.5511874
## Max.cor.Y.rcv.1X1###glmnet 0.7875648 0.5478979
## Low.cor.X##rcv#glmnet 0.7875648 0.5465340
## Random###myrandom_classfr 0.7875648 0.5012035
## MFO###myMFO_classfr 0.7875648 0.5000000
## Max.cor.Y##rcv#rpart 0.7875648 0.5000000
## Interact.High.cor.Y##rcv#glmnet 0.7875648 0.4963094
## Final.All.X###glmnet NA NA
## max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet 0.4967105 22.489
## Max.cor.Y.rcv.1X1###glmnet 0.5000000 0.788
## Low.cor.X##rcv#glmnet 0.4967105 26.852
## Random###myrandom_classfr 0.4922978 0.267
## MFO###myMFO_classfr 0.5000000 0.429
## Max.cor.Y##rcv#rpart 0.5000000 1.576
## Interact.High.cor.Y##rcv#glmnet 0.5000000 1.724
## Final.All.X###glmnet NA 2.429
## max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet 0.8069228 0.25
## Max.cor.Y.rcv.1X1###glmnet 0.8060109 0.50
## Low.cor.X##rcv#glmnet 0.8069191 0.30
## Random###myrandom_classfr 0.8060109 0.85
## MFO###myMFO_classfr 0.8060109 0.50
## Max.cor.Y##rcv#rpart 0.8060121 0.50
## Interact.High.cor.Y##rcv#glmnet 0.8060121 0.50
## Final.All.X###glmnet 0.8075676 0.25
## opt.prob.threshold.OOB
## All.X##rcv#glmnet 0.60
## Max.cor.Y.rcv.1X1###glmnet 0.50
## Low.cor.X##rcv#glmnet 0.65
## Random###myrandom_classfr 0.85
## MFO###myMFO_classfr 0.50
## Max.cor.Y##rcv#rpart 0.50
## Interact.High.cor.Y##rcv#glmnet 0.50
## Final.All.X###glmnet NA## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference D R
## D 152 0
## R 41 0## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy 1.415925 1.243398 2.711939 NA
## N 10.618246 3.510021 14.553264 NA
## MKy 52.182641 15.160971 68.876543 NA
## SKy 9.444703 2.878612 12.628212 NA
## MKn 31.035907 7.342244 39.517054 NA
## SKn 105.501365 29.134241 135.798491 NA
## PKn 8.686566 2.779787 12.238816 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.OOB
## PKy 0.01092896 0.01554404 0.00896861 8 2 3
## N 0.05464481 0.04663212 0.04484305 40 10 9
## MKy 0.24863388 0.24352332 0.24663677 182 55 47
## SKy 0.05054645 0.04663212 0.04484305 37 10 9
## MKn 0.13661202 0.11917098 0.11659193 100 26 23
## SKn 0.44262295 0.48186528 0.49327354 324 110 93
## PKn 0.05601093 0.04663212 0.04484305 41 10 9
## .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy 10 1 2 8 2 11 0.4144660
## N 40 9 10 40 10 49 0.3900023
## MKy 186 43 55 182 55 229 0.3225738
## SKy 39 7 10 37 10 46 0.3198458
## MKn 97 26 26 100 26 123 0.3192280
## SKn 325 92 110 324 110 417 0.3132714
## PKn 45 5 10 41 10 50 0.3088652
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy 0.1769906 NA 0.2465399
## N 0.2654561 NA 0.2970054
## MKy 0.2867178 NA 0.3007709
## SKy 0.2552622 NA 0.2745263
## MKn 0.3103591 NA 0.3212769
## SKn 0.3256215 NA 0.3256559
## PKn 0.2118675 NA 0.2447763
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 218.885353 62.049273 286.324318 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 732.000000
## .n.New.D .n.OOB .n.Trn.D .n.Trn.R
## 223.000000 193.000000 742.000000 183.000000
## .n.Tst .n.fit .n.new .n.trn
## 223.000000 732.000000 223.000000 925.000000
## err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## 2.388253 1.832275 NA 2.010552## [1] "Features Importance for selected models:"## All.X..rcv.glmnet.imp
## Hhold.fctrN:.clusterid.fctr6 100.000000
## Edn.fctr.Q 17.453730
## Q98197.fctrNo 14.402766
## Q118232.fctrId 12.858478
## Edn.fctr^5 12.015604
## Q120194.fctrStudy first 6.689618
## Q112478.fctrNo 6.116418
## Final.All.X...glmnet.imp
## Hhold.fctrN:.clusterid.fctr6 100.00000
## Edn.fctr.Q 16.76565
## Q98197.fctrNo 13.27072
## Q118232.fctrId 29.41524
## Edn.fctr^5 16.06488
## Q120194.fctrStudy first 16.66455
## Q112478.fctrNo 12.60934## [1] "glbObsNew prediction stats:"##
## D R
## 223 0## label step_major step_minor label_minor bgn end
## 10 predict.data.new 6 0 0 196.152 205.837
## 11 display.session.info 7 0 0 205.837 NA
## elapsed
## 10 9.685
## 11 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 4 fit.models 4 0 0 51.324
## 5 fit.models 4 1 1 131.607
## 1 cluster.data 1 0 0 9.273
## 2 partition.data.training 2 0 0 29.707
## 10 predict.data.new 6 0 0 196.152
## 8 fit.data.training 5 0 0 181.847
## 6 fit.models 4 2 2 171.471
## 9 fit.data.training 5 1 1 190.474
## 7 fit.models 4 3 3 178.930
## 3 select.features 3 0 0 48.833
## end elapsed duration
## 4 131.606 80.282 80.282
## 5 171.470 39.863 39.863
## 1 29.707 20.434 20.434
## 2 48.832 19.125 19.125
## 10 205.837 9.685 9.685
## 8 190.473 8.627 8.626
## 6 178.930 7.459 7.459
## 9 196.152 5.678 5.678
## 7 181.846 2.916 2.916
## 3 51.324 2.491 2.491
## [1] "Total Elapsed Time: 205.837 secs"
## label step_major step_minor label_minor
## 6 fit.models_0_Low.cor.X 1 5 glmnet
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## 5 fit.models_0_Interact.High.cor.Y 1 4 glmnet
## 2 fit.models_0_MFO 1 1 myMFO_classfr
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 1 fit.models_0_bgn 1 0 setup
## bgn end elapsed duration
## 6 93.830 131.592 37.763 37.762
## 4 67.086 84.218 17.132 17.132
## 5 84.219 93.830 9.611 9.611
## 2 51.886 59.540 7.654 7.654
## 3 59.541 67.085 7.544 7.544
## 1 51.853 51.886 0.033 0.033
## [1] "Total Elapsed Time: 131.592 secs"